{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial on self-normalizing networks on the CIFAR-10 data set\n",
    "\n",
    "tested with Python 3.5 and Tensorflow 1.1\n",
    "\n",
    "Adapted from CIFAR10 tutorial from [exelban](https://github.com/exelban/tensorflow-cifar-10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Fetch Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "import sys\n",
    "import tarfile\n",
    "import zipfile\n",
    "from urllib.request import urlretrieve\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def get_data_set(name=\"train\", cifar=10):\n",
    "    x = None\n",
    "    y = None\n",
    "    l = None\n",
    "    \n",
    "    maybe_download_and_extract()\n",
    "    \n",
    "    folder_name = \"cifar_10\" if cifar == 10 else \"cifar_100\"\n",
    "    \n",
    "    f = open('./data_set/' + folder_name + '/batches.meta', 'rb')\n",
    "    datadict = pickle.load(f, encoding='latin1')\n",
    "    f.close()\n",
    "    l = datadict['label_names']\n",
    "    \n",
    "    # mean and sdev of training set\n",
    "    mean_train = 0.4733630004850902\n",
    "    sdev_train = 0.2515689250632212\n",
    "    \n",
    "    if name is \"train\":\n",
    "        for i in range(5):\n",
    "            f = open('./data_set/' + folder_name + '/data_batch_' + str(i + 1), 'rb')\n",
    "            datadict = pickle.load(f, encoding='latin1')\n",
    "            f.close()\n",
    "            \n",
    "            _X = datadict[\"data\"]\n",
    "            _Y = datadict['labels']\n",
    "            \n",
    "            _X = np.array(_X, dtype=float) / 255.0\n",
    "            _X = _X.reshape([-1, 3, 32, 32])\n",
    "            _X = _X.transpose([0, 2, 3, 1])\n",
    "            _X = _X.reshape(-1, 32 * 32 * 3)\n",
    "            \n",
    "            if x is None:\n",
    "                x = _X\n",
    "                y = _Y\n",
    "            else:\n",
    "                x = np.concatenate((x, _X), axis=0)\n",
    "                y = np.concatenate((y, _Y), axis=0)\n",
    "        \n",
    "        # Normalize Data to mean = 0, stdev = 1\n",
    "        x = (x - mean_train) / sdev_train\n",
    "    \n",
    "    elif name is \"test\":\n",
    "        f = open('./data_set/' + folder_name + '/test_batch', 'rb')\n",
    "        datadict = pickle.load(f, encoding='latin1')\n",
    "        f.close()\n",
    "        \n",
    "        x = datadict[\"data\"]\n",
    "        y = np.array(datadict['labels'])\n",
    "        \n",
    "        x = np.array(x, dtype=float) / 255.0\n",
    "        x = x.reshape([-1, 3, 32, 32])\n",
    "        x = x.transpose([0, 2, 3, 1])\n",
    "        x = x.reshape(-1, 32 * 32 * 3)\n",
    "        \n",
    "        # Normalize Data according to mean and sdev of training set\n",
    "        x = (x - mean_train) / sdev_train\n",
    "    \n",
    "    def dense_to_one_hot(labels_dense, num_classes=10):\n",
    "        num_labels = labels_dense.shape[0]\n",
    "        index_offset = np.arange(num_labels) * num_classes\n",
    "        labels_one_hot = np.zeros((num_labels, num_classes))\n",
    "        labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n",
    "        \n",
    "        return labels_one_hot\n",
    "    \n",
    "    return x, dense_to_one_hot(y), l\n",
    "\n",
    "\n",
    "def _print_download_progress(count, block_size, total_size):\n",
    "    pct_complete = float(count * block_size) / total_size\n",
    "    msg = \"\\r- Download progress: {0:.1%}\".format(pct_complete)\n",
    "    sys.stdout.write(msg)\n",
    "    sys.stdout.flush()\n",
    "\n",
    "\n",
    "def maybe_download_and_extract():\n",
    "    main_directory = \"./data_set/\"\n",
    "    cifar_10_directory = main_directory + \"cifar_10/\"\n",
    "    cifar_100_directory = main_directory + \"cifar_100/\"\n",
    "    if not os.path.exists(main_directory):\n",
    "        os.makedirs(main_directory)\n",
    "        \n",
    "        url = \"http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\"\n",
    "        filename = url.split('/')[-1]\n",
    "        file_path = os.path.join(main_directory, filename)\n",
    "        zip_cifar_10 = file_path\n",
    "        file_path, _ = urlretrieve(url=url, filename=file_path, reporthook=_print_download_progress)\n",
    "        \n",
    "        print()\n",
    "        print(\"Download finished. Extracting files.\")\n",
    "        if file_path.endswith(\".zip\"):\n",
    "            zipfile.ZipFile(file=file_path, mode=\"r\").extractall(main_directory)\n",
    "        elif file_path.endswith((\".tar.gz\", \".tgz\")):\n",
    "            tarfile.open(name=file_path, mode=\"r:gz\").extractall(main_directory)\n",
    "        print(\"Done.\")\n",
    "        \n",
    "        url = \"http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz\"\n",
    "        filename = url.split('/')[-1]\n",
    "        file_path = os.path.join(main_directory, filename)\n",
    "        zip_cifar_100 = file_path\n",
    "        file_path, _ = urlretrieve(url=url, filename=file_path, reporthook=_print_download_progress)\n",
    "        \n",
    "        print()\n",
    "        print(\"Download finished. Extracting files.\")\n",
    "        if file_path.endswith(\".zip\"):\n",
    "            zipfile.ZipFile(file=file_path, mode=\"r\").extractall(main_directory)\n",
    "        elif file_path.endswith((\".tar.gz\", \".tgz\")):\n",
    "            tarfile.open(name=file_path, mode=\"r:gz\").extractall(main_directory)\n",
    "        print(\"Done.\")\n",
    "        \n",
    "        os.rename(main_directory + \"./cifar-10-batches-py\", cifar_10_directory)\n",
    "        os.rename(main_directory + \"./cifar-100-python\", cifar_100_directory)\n",
    "        os.remove(zip_cifar_10)\n",
    "        os.remove(zip_cifar_100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaled ELU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.python.framework import ops\n",
    "\n",
    "\n",
    "def selu(x, name=\"selu\"):\n",
    "    \"\"\" When using SELUs you have to keep the following in mind:\n",
    "    # (1) scale inputs to zero mean and unit variance\n",
    "    # (2) use SELUs\n",
    "    # (3) initialize weights with stddev sqrt(1/n)\n",
    "    # (4) use SELU dropout\n",
    "    \"\"\"\n",
    "    with ops.name_scope(name) as scope:\n",
    "        alpha = 1.6732632423543772848170429916717\n",
    "        scale = 1.0507009873554804934193349852946\n",
    "        return scale * tf.where(x >= 0.0, x, alpha * tf.nn.elu(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some helpers to build the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from math import sqrt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "\n",
    "def _variable_with_weight_decay(name, shape, activation, stddev, wd=None):    \n",
    "    # Determine number of input features from shape\n",
    "    f_in = np.prod(shape[:-1]) if len(shape) == 4 else shape[0]\n",
    "    \n",
    "    # Calculate sdev for initialization according to activation function\n",
    "    if activation == selu:\n",
    "        sdev = sqrt(1 / f_in)\n",
    "    elif activation == tf.nn.relu:\n",
    "        sdev = sqrt(2 / f_in)\n",
    "    elif activation == tf.nn.elu:\n",
    "        sdev = sqrt(1.5505188080679277 / f_in)\n",
    "    else:\n",
    "        sdev = stddev\n",
    "    \n",
    "    var = tf.get_variable(name=name, shape=shape,\n",
    "                          initializer=tf.truncated_normal_initializer(stddev=sdev, dtype=tf.float32))\n",
    "    if wd is not None:\n",
    "        weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')\n",
    "        tf.add_to_collection('losses', weight_decay)\n",
    "    return var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "\n",
    "def conv2d(scope_name, input, activation, ksize, f_in, f_out, bias_init=0.0, stddev=5e-2):\n",
    "    with tf.variable_scope(scope_name) as scope:\n",
    "        kernel = _variable_with_weight_decay('weights', shape=[ksize, ksize, f_in, f_out], activation=activation,\n",
    "                                             stddev=stddev)\n",
    "        conv = tf.nn.conv2d(input, kernel, [1, 1, 1, 1], padding='SAME')\n",
    "        biases = tf.get_variable('biases', [f_out], initializer=tf.constant_initializer(bias_init), dtype=tf.float32)\n",
    "        pre_activation = tf.nn.bias_add(conv, biases)\n",
    "        return activation(pre_activation, name=scope.name)\n",
    "\n",
    "\n",
    "def fc(scope_name, input, activation, n_in, n_out, stddev=0.04, bias_init=0.0, weight_decay=None):\n",
    "    with tf.variable_scope(scope_name) as scope:\n",
    "        weights = _variable_with_weight_decay('weights', shape=[n_in, n_out], activation=activation, stddev=stddev,\n",
    "                                              wd=weight_decay)\n",
    "        biases = tf.get_variable(name='biases', shape=[n_out], initializer=tf.constant_initializer(bias_init),\n",
    "                                 dtype=tf.float32)\n",
    "        return activation(tf.matmul(input, weights) + biases, name=scope.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the model with a specified activation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model(activation):\n",
    "    _IMAGE_SIZE = 32\n",
    "    _IMAGE_CHANNELS = 3\n",
    "    _NUM_CLASSES = 10\n",
    "    _RESHAPE_SIZE = 4 * 4 * 128\n",
    "    \n",
    "    # set activation function\n",
    "    act = selu if activation == \"selu\" else tf.nn.elu if activation == \"elu\" else tf.nn.relu\n",
    "    \n",
    "    with tf.variable_scope(activation):\n",
    "        # input\n",
    "        with tf.name_scope('data'):\n",
    "            x = tf.placeholder(tf.float32, shape=[None, _IMAGE_SIZE * _IMAGE_SIZE * _IMAGE_CHANNELS], name='Input')\n",
    "            y = tf.placeholder(tf.float32, shape=[None, _NUM_CLASSES], name='Output')\n",
    "            x_image = tf.reshape(x, [-1, _IMAGE_SIZE, _IMAGE_SIZE, _IMAGE_CHANNELS], name='images')\n",
    "        \n",
    "        # Conv 1\n",
    "        conv1 = conv2d(\"conv1\", input=x_image, activation=act, ksize=5, f_in=3, f_out=64)\n",
    "        pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1')\n",
    "        \n",
    "        # Conv 2\n",
    "        conv2 = conv2d(\"conv2\", input=pool1, activation=act, ksize=5, f_in=64, f_out=64, bias_init=0.1)\n",
    "        pool2 = tf.nn.max_pool(conv2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool2')\n",
    "        \n",
    "        # Conv 3-5\n",
    "        conv3 = conv2d(\"conv3\", input=pool2, activation=act, ksize=3, f_in=64, f_out=128)\n",
    "        conv4 = conv2d(\"conv4\", input=conv3, activation=act, ksize=3, f_in=128, f_out=128)\n",
    "        conv5 = conv2d(\"conv5\", input=conv4, activation=act, ksize=3, f_in=128, f_out=128)\n",
    "        \n",
    "        # Pool\n",
    "        pool3 = tf.nn.max_pool(conv5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool3')\n",
    "        \n",
    "        # Reshape\n",
    "        reshape = tf.reshape(pool3, [-1, _RESHAPE_SIZE])\n",
    "        dim = reshape.get_shape()[1].value\n",
    "        \n",
    "        # Fully Connected\n",
    "        fc1 = fc('fully_connected1', input=reshape, activation=act, n_in=dim, n_out=384, stddev=0.04, bias_init=0.1,\n",
    "                 weight_decay=0.004)\n",
    "        fc2 = fc('fully_connected2', input=fc1, activation=act, n_in=384, n_out=192, stddev=0.04, bias_init=0.1,\n",
    "                 weight_decay=0.004)\n",
    "        \n",
    "        # Softmax\n",
    "        with tf.variable_scope('output') as scope:\n",
    "            weights = _variable_with_weight_decay('weights', [192, _NUM_CLASSES], stddev=1 / 192.0,\n",
    "                                                  activation=activation,\n",
    "                                                  wd=0.0)\n",
    "            biases = tf.get_variable(name='biases', shape=[_NUM_CLASSES], initializer=tf.constant_initializer(0.0),\n",
    "                                     dtype=tf.float32)\n",
    "            softmax_linear = tf.add(tf.matmul(fc2, weights), biases, name=scope.name)\n",
    "            \n",
    "            # output\n",
    "            y_pred_cls = tf.argmax(softmax_linear, dimension=1)\n",
    "        \n",
    "        # Define Loss and Optimizer\n",
    "        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=softmax_linear, labels=y))\n",
    "        optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)\n",
    "        \n",
    "        correct_prediction = tf.equal(y_pred_cls, tf.argmax(y, dimension=1))\n",
    "        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "        # tf.summary.scalar(\"Accuracy/train\", accuracy)\n",
    "    \n",
    "    return {\"x\": x, \"y\": y, \"output\": y_pred_cls, \"loss\": loss, \"accuracy\": accuracy, \"optimizer\": optimizer, \"name\": activation}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate on Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict_test(test_x, test_y, models):\n",
    "    \"\"\"\n",
    "        Make prediction for all images in test_x\n",
    "    \"\"\"\n",
    "    i = 0\n",
    "    predicted_class = {\"selu\": np.zeros(shape=len(test_x), dtype=np.int), \n",
    "                       \"elu\": np.zeros(shape=len(test_x), dtype=np.int), \n",
    "                       \"relu\":np.zeros(shape=len(test_x), dtype=np.int)}\n",
    "    while i < len(test_x):\n",
    "        j = min(i + _BATCH_SIZE, len(test_x))\n",
    "        batch_xs = test_x[i:j, :]\n",
    "        batch_ys = test_y[i:j, :]\n",
    "        for name, model in models.items():\n",
    "            predicted_class[name][i:j] = sess.run(model[\"output\"], feed_dict={model['x']: batch_xs, model['y']: batch_ys})\n",
    "        i = j\n",
    "    \n",
    "    accuracy = {\"selu\": 0, \"elu\": 0, \"relu\": 0}\n",
    "    for name, model in models.items():\n",
    "        correct = (np.argmax(test_y, axis=1) == predicted_class[name])        \n",
    "        accuracy[name] = correct.mean() * 100        \n",
    "    \n",
    "    print(\"Accuracy on Test-Set (SELU/ELU/RELU): {0:.2f}% | {1:.2f}% | {2:.2f}%\".format(\n",
    "        accuracy[\"selu\"], accuracy[\"elu\"], accuracy[\"relu\"]))\n",
    "    \n",
    "    return accuracy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def plot_metric(title, ylabel, metric):\n",
    "    # Training Accuracy\n",
    "    plt.figure()    \n",
    "    plt.title(title, size=\"xx-large\")\n",
    "    plt.ylabel(ylabel, size=\"x-large\")    \n",
    "    plt.tick_params(axis=\"x\", bottom=\"off\", labelbottom=\"off\")\n",
    "    \n",
    "    # select manually for consistent colors\n",
    "    plt.plot(metric[\"selu\"], label=\"SELU\", linewidth=2)\n",
    "    plt.plot(metric[\"elu\"], label=\"ELU\", linewidth=2)\n",
    "    plt.plot(metric[\"relu\"], label=\"RELU\", linewidth=2)\n",
    "        \n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "def plot(train_loss, train_accuracy, test_accuracy):    \n",
    "    # Training Loss\n",
    "    plot_metric(\"Training Loss\", \"Loss\", train_loss)\n",
    "    \n",
    "    # Training Accuracy\n",
    "    plot_metric(\"Training Accuracy\", \"Accuracy\", train_accuracy)\n",
    "    \n",
    "    # Test Accuracy\n",
    "    plot_metric(\"Test Accuracy\", \"Accuracy\", test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train(session, num_iterations, train_x, train_y, test_x, test_y, models, global_step):\n",
    "    \"\"\"\n",
    "        Train CNN\n",
    "    \"\"\"    \n",
    "    train_loss = {\"selu\": [], \"elu\":[], \"relu\": []}\n",
    "    train_accuracy = {\"selu\": [], \"elu\":[], \"relu\": []}    \n",
    "    test_accuracy = {\"selu\": [], \"elu\":[], \"relu\": []}\n",
    "    \n",
    "    inc_step_op = tf.assign(global_step, global_step+1)\n",
    "    # start training\n",
    "    for i in range(num_iterations):\n",
    "        randidx = np.random.randint(len(train_x), size=_BATCH_SIZE)\n",
    "        batch_xs = train_x[randidx]\n",
    "        batch_ys = train_y[randidx]\n",
    "                \n",
    "        optimizers = []\n",
    "        feed_dict = {}\n",
    "        for name, model in models.items():\n",
    "            optimizers.append(model[\"optimizer\"])\n",
    "            feed_dict.update({model[\"x\"]: batch_xs, model[\"y\"]: batch_ys})\n",
    "        \n",
    "        # current step\n",
    "        i_global = session.run(global_step)\n",
    "        \n",
    "        # train\n",
    "        session.run( optimizers, feed_dict=feed_dict)\n",
    "        \n",
    "        # print training loss\n",
    "        if (i_global % 10 == 0) or (i == num_iterations - 1):\n",
    "            l_selu, l_elu, l_relu, acc_selu, acc_elu, acc_relu = session.run(\n",
    "                [models['selu']['loss'], models['elu']['loss'], models['relu']['loss'], \n",
    "                 models['selu']['accuracy'], models['elu']['accuracy'], models['relu']['accuracy']],\n",
    "                feed_dict=feed_dict)\n",
    "            \n",
    "            msg = \"Global Step: {0:>6}, \" \\\n",
    "                  \"accuracy (SELU/ELU/RELU): {1:>6.1%} | {2:>6.1%} | {3:>6.1%}, \" \\\n",
    "                  \"loss (SELU/ELU/RELU): {4:.2f} | {5:.2f} | {6:.2f}\"\n",
    "            print(msg.format(i_global, acc_selu, acc_elu, acc_relu, l_selu, l_elu, l_relu))            \n",
    "            \n",
    "            # collect metrics for plots                            \n",
    "            train_loss[\"selu\"].append(l_selu)\n",
    "            train_loss[\"elu\"].append(l_elu)\n",
    "            train_loss[\"relu\"].append(l_relu)\n",
    "            train_accuracy[\"selu\"].append(acc_selu)\n",
    "            train_accuracy[\"elu\"].append(acc_elu)\n",
    "            train_accuracy[\"relu\"].append(acc_relu)\n",
    "\n",
    "        # evaluate test set accuracy\n",
    "        if (i_global % 100 == 0) or (i == num_iterations - 1):\n",
    "            acc = predict_test(test_x, test_y, models)                \n",
    "            test_accuracy[\"selu\"].append(acc[\"selu\"])\n",
    "            test_accuracy[\"elu\"].append(acc[\"elu\"])\n",
    "            test_accuracy[\"relu\"].append(acc[\"relu\"])\n",
    "            saver.save(session, save_path=_SAVE_PATH + \"/checkpoint\", global_step=global_step)\n",
    "            print(\"Saved checkpoint.\")\n",
    "        \n",
    "        # increment global step\n",
    "        session.run(inc_step_op)\n",
    "    return train_loss, train_accuracy, test_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "_IMG_SIZE = 32\n",
    "_NUM_CHANNELS = 3\n",
    "_BATCH_SIZE = 128\n",
    "_CLASS_SIZE = 10\n",
    "_ITERATION = 10000\n",
    "_SAVE_PATH = \"./checkpoints/cifar-10\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# Set GPU\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "\n",
    "if not os.path.exists(_SAVE_PATH):\n",
    "    os.makedirs(_SAVE_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# Build Graph\n",
    "relu = model(\"relu\")\n",
    "selu = model(\"selu\")\n",
    "elu = model(\"elu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trying to restore last checkpoint ...\n",
      "INFO:tensorflow:Restoring parameters from ./checkpoints/cifar-10/checkpoint-400\n",
      "Restored checkpoint from: ./checkpoints/cifar-10/checkpoint-400\n",
      "Global Step:    400, accuracy (SELU/ELU/RELU):  57.0% |  57.0% |  55.5%, loss (SELU/ELU/RELU): 1.10 | 1.19 | 1.33\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 53.89% | 52.13% | 48.64%\n",
      "Saved checkpoint.\n",
      "Global Step:    410, accuracy (SELU/ELU/RELU):  57.0% |  53.1% |  53.9%, loss (SELU/ELU/RELU): 1.05 | 1.14 | 1.17\n",
      "Global Step:    420, accuracy (SELU/ELU/RELU):  60.2% |  54.7% |  57.8%, loss (SELU/ELU/RELU): 1.07 | 1.15 | 1.29\n",
      "Global Step:    430, accuracy (SELU/ELU/RELU):  59.4% |  55.5% |  52.3%, loss (SELU/ELU/RELU): 1.19 | 1.19 | 1.37\n",
      "Global Step:    440, accuracy (SELU/ELU/RELU):  52.3% |  55.5% |  50.8%, loss (SELU/ELU/RELU): 1.22 | 1.22 | 1.35\n",
      "Global Step:    450, accuracy (SELU/ELU/RELU):  62.5% |  58.6% |  52.3%, loss (SELU/ELU/RELU): 1.10 | 1.12 | 1.24\n",
      "Global Step:    460, accuracy (SELU/ELU/RELU):  58.6% |  54.7% |  49.2%, loss (SELU/ELU/RELU): 1.21 | 1.30 | 1.43\n",
      "Global Step:    470, accuracy (SELU/ELU/RELU):  60.9% |  60.9% |  51.6%, loss (SELU/ELU/RELU): 1.13 | 1.17 | 1.40\n",
      "Global Step:    480, accuracy (SELU/ELU/RELU):  61.7% |  59.4% |  53.9%, loss (SELU/ELU/RELU): 1.23 | 1.30 | 1.41\n",
      "Global Step:    490, accuracy (SELU/ELU/RELU):  54.7% |  53.9% |  46.9%, loss (SELU/ELU/RELU): 1.21 | 1.28 | 1.41\n",
      "Global Step:    500, accuracy (SELU/ELU/RELU):  57.0% |  52.3% |  46.9%, loss (SELU/ELU/RELU): 1.23 | 1.35 | 1.50\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 54.94% | 54.19% | 48.66%\n",
      "Saved checkpoint.\n",
      "Global Step:    510, accuracy (SELU/ELU/RELU):  53.9% |  55.5% |  53.1%, loss (SELU/ELU/RELU): 1.16 | 1.20 | 1.38\n",
      "Global Step:    520, accuracy (SELU/ELU/RELU):  57.8% |  54.7% |  48.4%, loss (SELU/ELU/RELU): 1.28 | 1.32 | 1.46\n",
      "Global Step:    530, accuracy (SELU/ELU/RELU):  60.9% |  58.6% |  51.6%, loss (SELU/ELU/RELU): 1.10 | 1.09 | 1.28\n",
      "Global Step:    540, accuracy (SELU/ELU/RELU):  54.7% |  50.8% |  47.7%, loss (SELU/ELU/RELU): 1.34 | 1.43 | 1.52\n",
      "Global Step:    550, accuracy (SELU/ELU/RELU):  54.7% |  51.6% |  52.3%, loss (SELU/ELU/RELU): 1.15 | 1.23 | 1.35\n",
      "Global Step:    560, accuracy (SELU/ELU/RELU):  57.0% |  55.5% |  51.6%, loss (SELU/ELU/RELU): 1.06 | 1.15 | 1.28\n",
      "Global Step:    570, accuracy (SELU/ELU/RELU):  64.1% |  66.4% |  59.4%, loss (SELU/ELU/RELU): 0.98 | 1.06 | 1.12\n",
      "Global Step:    580, accuracy (SELU/ELU/RELU):  62.5% |  56.2% |  53.1%, loss (SELU/ELU/RELU): 1.16 | 1.26 | 1.44\n",
      "Global Step:    590, accuracy (SELU/ELU/RELU):  63.3% |  67.2% |  54.7%, loss (SELU/ELU/RELU): 1.06 | 1.06 | 1.26\n",
      "Global Step:    600, accuracy (SELU/ELU/RELU):  66.4% |  62.5% |  59.4%, loss (SELU/ELU/RELU): 1.09 | 1.12 | 1.24\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 59.43% | 58.15% | 53.50%\n",
      "Saved checkpoint.\n",
      "Global Step:    610, accuracy (SELU/ELU/RELU):  61.7% |  53.1% |  48.4%, loss (SELU/ELU/RELU): 1.00 | 1.15 | 1.30\n",
      "Global Step:    620, accuracy (SELU/ELU/RELU):  52.3% |  49.2% |  45.3%, loss (SELU/ELU/RELU): 1.23 | 1.33 | 1.43\n",
      "Global Step:    630, accuracy (SELU/ELU/RELU):  59.4% |  66.4% |  57.0%, loss (SELU/ELU/RELU): 1.02 | 1.01 | 1.19\n",
      "Global Step:    640, accuracy (SELU/ELU/RELU):  56.2% |  60.9% |  57.8%, loss (SELU/ELU/RELU): 1.19 | 1.16 | 1.24\n",
      "Global Step:    650, accuracy (SELU/ELU/RELU):  60.9% |  64.1% |  48.4%, loss (SELU/ELU/RELU): 1.08 | 1.10 | 1.31\n",
      "Global Step:    660, accuracy (SELU/ELU/RELU):  57.0% |  57.0% |  51.6%, loss (SELU/ELU/RELU): 1.08 | 1.13 | 1.26\n",
      "Global Step:    670, accuracy (SELU/ELU/RELU):  64.1% |  64.1% |  50.0%, loss (SELU/ELU/RELU): 1.10 | 1.11 | 1.29\n",
      "Global Step:    680, accuracy (SELU/ELU/RELU):  59.4% |  59.4% |  50.0%, loss (SELU/ELU/RELU): 1.06 | 1.05 | 1.25\n",
      "Global Step:    690, accuracy (SELU/ELU/RELU):  64.8% |  59.4% |  57.0%, loss (SELU/ELU/RELU): 0.94 | 1.06 | 1.18\n",
      "Global Step:    700, accuracy (SELU/ELU/RELU):  60.9% |  54.7% |  57.0%, loss (SELU/ELU/RELU): 1.10 | 1.18 | 1.34\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 60.28% | 57.94% | 52.54%\n",
      "Saved checkpoint.\n",
      "Global Step:    710, accuracy (SELU/ELU/RELU):  61.7% |  64.1% |  62.5%, loss (SELU/ELU/RELU): 1.17 | 1.18 | 1.30\n",
      "Global Step:    720, accuracy (SELU/ELU/RELU):  63.3% |  68.0% |  57.8%, loss (SELU/ELU/RELU): 1.05 | 1.06 | 1.15\n",
      "Global Step:    730, accuracy (SELU/ELU/RELU):  58.6% |  60.2% |  53.9%, loss (SELU/ELU/RELU): 1.06 | 1.04 | 1.20\n",
      "Global Step:    740, accuracy (SELU/ELU/RELU):  63.3% |  61.7% |  61.7%, loss (SELU/ELU/RELU): 0.99 | 1.01 | 1.09\n",
      "Global Step:    750, accuracy (SELU/ELU/RELU):  69.5% |  64.1% |  64.1%, loss (SELU/ELU/RELU): 0.94 | 0.98 | 1.12\n",
      "Global Step:    760, accuracy (SELU/ELU/RELU):  59.4% |  60.9% |  60.9%, loss (SELU/ELU/RELU): 1.00 | 1.05 | 1.15\n",
      "Global Step:    770, accuracy (SELU/ELU/RELU):  66.4% |  60.9% |  57.0%, loss (SELU/ELU/RELU): 0.98 | 1.03 | 1.22\n",
      "Global Step:    780, accuracy (SELU/ELU/RELU):  65.6% |  62.5% |  66.4%, loss (SELU/ELU/RELU): 0.91 | 0.98 | 1.11\n",
      "Global Step:    790, accuracy (SELU/ELU/RELU):  68.8% |  67.2% |  52.3%, loss (SELU/ELU/RELU): 0.95 | 0.97 | 1.19\n",
      "Global Step:    800, accuracy (SELU/ELU/RELU):  70.3% |  70.3% |  56.2%, loss (SELU/ELU/RELU): 0.93 | 0.95 | 1.19\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 61.16% | 60.48% | 53.96%\n",
      "Saved checkpoint.\n",
      "Global Step:    810, accuracy (SELU/ELU/RELU):  69.5% |  68.8% |  60.2%, loss (SELU/ELU/RELU): 0.88 | 0.94 | 1.06\n",
      "Global Step:    820, accuracy (SELU/ELU/RELU):  64.8% |  68.8% |  59.4%, loss (SELU/ELU/RELU): 1.07 | 1.07 | 1.18\n",
      "Global Step:    830, accuracy (SELU/ELU/RELU):  64.8% |  64.8% |  60.9%, loss (SELU/ELU/RELU): 0.87 | 0.95 | 1.18\n",
      "Global Step:    840, accuracy (SELU/ELU/RELU):  64.1% |  61.7% |  53.9%, loss (SELU/ELU/RELU): 0.98 | 1.09 | 1.26\n",
      "Global Step:    850, accuracy (SELU/ELU/RELU):  68.0% |  65.6% |  66.4%, loss (SELU/ELU/RELU): 0.94 | 1.00 | 1.05\n",
      "Global Step:    860, accuracy (SELU/ELU/RELU):  72.7% |  63.3% |  67.2%, loss (SELU/ELU/RELU): 0.80 | 0.93 | 1.01\n",
      "Global Step:    870, accuracy (SELU/ELU/RELU):  65.6% |  61.7% |  60.2%, loss (SELU/ELU/RELU): 1.13 | 1.22 | 1.33\n",
      "Global Step:    880, accuracy (SELU/ELU/RELU):  61.7% |  63.3% |  52.3%, loss (SELU/ELU/RELU): 1.06 | 1.06 | 1.19\n",
      "Global Step:    890, accuracy (SELU/ELU/RELU):  64.8% |  61.7% |  53.1%, loss (SELU/ELU/RELU): 1.03 | 1.07 | 1.28\n",
      "Global Step:    900, accuracy (SELU/ELU/RELU):  63.3% |  60.2% |  59.4%, loss (SELU/ELU/RELU): 1.08 | 1.14 | 1.27\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 63.19% | 62.05% | 56.31%\n",
      "Saved checkpoint.\n",
      "Global Step:    910, accuracy (SELU/ELU/RELU):  62.5% |  60.2% |  57.0%, loss (SELU/ELU/RELU): 1.12 | 1.16 | 1.35\n",
      "Global Step:    920, accuracy (SELU/ELU/RELU):  60.9% |  63.3% |  58.6%, loss (SELU/ELU/RELU): 1.01 | 1.03 | 1.16\n",
      "Global Step:    930, accuracy (SELU/ELU/RELU):  66.4% |  66.4% |  58.6%, loss (SELU/ELU/RELU): 0.99 | 0.98 | 1.11\n",
      "Global Step:    940, accuracy (SELU/ELU/RELU):  68.0% |  65.6% |  61.7%, loss (SELU/ELU/RELU): 0.87 | 0.95 | 1.03\n",
      "Global Step:    950, accuracy (SELU/ELU/RELU):  63.3% |  67.2% |  64.1%, loss (SELU/ELU/RELU): 1.00 | 0.98 | 1.12\n",
      "Global Step:    960, accuracy (SELU/ELU/RELU):  64.8% |  63.3% |  52.3%, loss (SELU/ELU/RELU): 0.97 | 1.09 | 1.25\n",
      "Global Step:    970, accuracy (SELU/ELU/RELU):  70.3% |  65.6% |  57.8%, loss (SELU/ELU/RELU): 0.95 | 1.01 | 1.13\n",
      "Global Step:    980, accuracy (SELU/ELU/RELU):  64.8% |  63.3% |  57.8%, loss (SELU/ELU/RELU): 1.01 | 1.08 | 1.22\n",
      "Global Step:    990, accuracy (SELU/ELU/RELU):  62.5% |  65.6% |  54.7%, loss (SELU/ELU/RELU): 1.01 | 1.06 | 1.19\n",
      "Global Step:   1000, accuracy (SELU/ELU/RELU):  67.2% |  62.5% |  61.7%, loss (SELU/ELU/RELU): 0.96 | 1.09 | 1.17\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 64.05% | 63.65% | 57.79%\n",
      "Saved checkpoint.\n",
      "Global Step:   1010, accuracy (SELU/ELU/RELU):  68.0% |  61.7% |  63.3%, loss (SELU/ELU/RELU): 0.97 | 1.04 | 1.18\n",
      "Global Step:   1020, accuracy (SELU/ELU/RELU):  68.8% |  64.8% |  54.7%, loss (SELU/ELU/RELU): 0.90 | 1.00 | 1.19\n",
      "Global Step:   1030, accuracy (SELU/ELU/RELU):  71.1% |  68.0% |  63.3%, loss (SELU/ELU/RELU): 0.82 | 0.96 | 1.07\n",
      "Global Step:   1040, accuracy (SELU/ELU/RELU):  67.2% |  65.6% |  62.5%, loss (SELU/ELU/RELU): 0.94 | 0.96 | 1.06\n",
      "Global Step:   1050, accuracy (SELU/ELU/RELU):  63.3% |  68.0% |  56.2%, loss (SELU/ELU/RELU): 0.95 | 0.99 | 1.17\n",
      "Global Step:   1060, accuracy (SELU/ELU/RELU):  64.8% |  64.8% |  66.4%, loss (SELU/ELU/RELU): 0.91 | 0.95 | 1.10\n",
      "Global Step:   1070, accuracy (SELU/ELU/RELU):  66.4% |  65.6% |  59.4%, loss (SELU/ELU/RELU): 0.91 | 0.93 | 1.15\n",
      "Global Step:   1080, accuracy (SELU/ELU/RELU):  71.1% |  66.4% |  60.9%, loss (SELU/ELU/RELU): 0.87 | 0.97 | 1.09\n",
      "Global Step:   1090, accuracy (SELU/ELU/RELU):  73.4% |  71.1% |  59.4%, loss (SELU/ELU/RELU): 0.85 | 0.89 | 1.08\n",
      "Global Step:   1100, accuracy (SELU/ELU/RELU):  67.2% |  64.8% |  57.0%, loss (SELU/ELU/RELU): 0.91 | 0.96 | 1.10\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 65.22% | 63.26% | 57.96%\n",
      "Saved checkpoint.\n",
      "Global Step:   1110, accuracy (SELU/ELU/RELU):  66.4% |  66.4% |  55.5%, loss (SELU/ELU/RELU): 0.91 | 0.99 | 1.22\n",
      "Global Step:   1120, accuracy (SELU/ELU/RELU):  71.1% |  71.9% |  64.8%, loss (SELU/ELU/RELU): 0.88 | 0.90 | 0.93\n",
      "Global Step:   1130, accuracy (SELU/ELU/RELU):  71.9% |  67.2% |  65.6%, loss (SELU/ELU/RELU): 0.90 | 0.94 | 0.98\n",
      "Global Step:   1140, accuracy (SELU/ELU/RELU):  68.8% |  70.3% |  55.5%, loss (SELU/ELU/RELU): 0.98 | 0.98 | 1.16\n",
      "Global Step:   1150, accuracy (SELU/ELU/RELU):  60.9% |  60.9% |  58.6%, loss (SELU/ELU/RELU): 1.03 | 1.08 | 1.22\n",
      "Global Step:   1160, accuracy (SELU/ELU/RELU):  63.3% |  68.0% |  63.3%, loss (SELU/ELU/RELU): 0.91 | 1.00 | 1.10\n",
      "Global Step:   1170, accuracy (SELU/ELU/RELU):  66.4% |  70.3% |  62.5%, loss (SELU/ELU/RELU): 0.85 | 0.86 | 1.06\n",
      "Global Step:   1180, accuracy (SELU/ELU/RELU):  68.0% |  68.0% |  64.1%, loss (SELU/ELU/RELU): 0.88 | 0.92 | 1.08\n",
      "Global Step:   1190, accuracy (SELU/ELU/RELU):  68.8% |  64.8% |  60.9%, loss (SELU/ELU/RELU): 0.92 | 0.98 | 1.10\n",
      "Global Step:   1200, accuracy (SELU/ELU/RELU):  66.4% |  67.2% |  65.6%, loss (SELU/ELU/RELU): 0.91 | 0.91 | 0.95\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 65.66% | 64.73% | 58.86%\n",
      "Saved checkpoint.\n",
      "Global Step:   1210, accuracy (SELU/ELU/RELU):  75.0% |  71.9% |  59.4%, loss (SELU/ELU/RELU): 0.77 | 0.91 | 1.00\n",
      "Global Step:   1220, accuracy (SELU/ELU/RELU):  66.4% |  62.5% |  59.4%, loss (SELU/ELU/RELU): 0.89 | 0.93 | 1.05\n",
      "Global Step:   1230, accuracy (SELU/ELU/RELU):  74.2% |  68.0% |  60.9%, loss (SELU/ELU/RELU): 0.81 | 0.92 | 1.05\n",
      "Global Step:   1240, accuracy (SELU/ELU/RELU):  69.5% |  71.9% |  68.0%, loss (SELU/ELU/RELU): 0.73 | 0.78 | 0.94\n",
      "Global Step:   1250, accuracy (SELU/ELU/RELU):  72.7% |  68.0% |  63.3%, loss (SELU/ELU/RELU): 0.92 | 0.92 | 1.12\n",
      "Global Step:   1260, accuracy (SELU/ELU/RELU):  77.3% |  75.0% |  71.9%, loss (SELU/ELU/RELU): 0.73 | 0.79 | 0.94\n",
      "Global Step:   1270, accuracy (SELU/ELU/RELU):  71.1% |  65.6% |  68.0%, loss (SELU/ELU/RELU): 0.84 | 0.90 | 1.09\n",
      "Global Step:   1280, accuracy (SELU/ELU/RELU):  65.6% |  63.3% |  63.3%, loss (SELU/ELU/RELU): 0.95 | 0.95 | 1.12\n",
      "Global Step:   1290, accuracy (SELU/ELU/RELU):  77.3% |  76.6% |  74.2%, loss (SELU/ELU/RELU): 0.65 | 0.69 | 0.79\n",
      "Global Step:   1300, accuracy (SELU/ELU/RELU):  73.4% |  73.4% |  68.8%, loss (SELU/ELU/RELU): 0.81 | 0.80 | 0.96\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 66.13% | 66.28% | 60.95%\n",
      "Saved checkpoint.\n",
      "Global Step:   1310, accuracy (SELU/ELU/RELU):  63.3% |  65.6% |  60.2%, loss (SELU/ELU/RELU): 0.98 | 1.02 | 1.20\n",
      "Global Step:   1320, accuracy (SELU/ELU/RELU):  70.3% |  68.0% |  69.5%, loss (SELU/ELU/RELU): 0.79 | 0.81 | 0.89\n",
      "Global Step:   1330, accuracy (SELU/ELU/RELU):  70.3% |  71.9% |  67.2%, loss (SELU/ELU/RELU): 0.81 | 0.88 | 1.02\n",
      "Global Step:   1340, accuracy (SELU/ELU/RELU):  69.5% |  66.4% |  63.3%, loss (SELU/ELU/RELU): 0.89 | 0.91 | 0.99\n",
      "Global Step:   1350, accuracy (SELU/ELU/RELU):  79.7% |  75.0% |  68.0%, loss (SELU/ELU/RELU): 0.70 | 0.71 | 0.87\n",
      "Global Step:   1360, accuracy (SELU/ELU/RELU):  67.2% |  69.5% |  65.6%, loss (SELU/ELU/RELU): 0.97 | 1.01 | 1.10\n",
      "Global Step:   1370, accuracy (SELU/ELU/RELU):  73.4% |  68.8% |  60.9%, loss (SELU/ELU/RELU): 0.80 | 0.82 | 1.10\n",
      "Global Step:   1380, accuracy (SELU/ELU/RELU):  68.8% |  68.8% |  62.5%, loss (SELU/ELU/RELU): 0.77 | 0.79 | 0.91\n",
      "Global Step:   1390, accuracy (SELU/ELU/RELU):  73.4% |  74.2% |  61.7%, loss (SELU/ELU/RELU): 0.79 | 0.84 | 1.00\n",
      "Global Step:   1400, accuracy (SELU/ELU/RELU):  69.5% |  69.5% |  59.4%, loss (SELU/ELU/RELU): 0.82 | 0.85 | 1.07\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 67.47% | 66.29% | 60.43%\n",
      "Saved checkpoint.\n",
      "Global Step:   1410, accuracy (SELU/ELU/RELU):  68.8% |  68.0% |  60.2%, loss (SELU/ELU/RELU): 0.90 | 0.91 | 1.02\n",
      "Global Step:   1420, accuracy (SELU/ELU/RELU):  64.8% |  64.8% |  58.6%, loss (SELU/ELU/RELU): 0.92 | 0.92 | 1.13\n",
      "Global Step:   1430, accuracy (SELU/ELU/RELU):  71.9% |  66.4% |  61.7%, loss (SELU/ELU/RELU): 0.85 | 0.86 | 1.07\n",
      "Global Step:   1440, accuracy (SELU/ELU/RELU):  69.5% |  68.8% |  64.1%, loss (SELU/ELU/RELU): 0.82 | 0.93 | 1.08\n",
      "Global Step:   1450, accuracy (SELU/ELU/RELU):  78.1% |  77.3% |  75.0%, loss (SELU/ELU/RELU): 0.65 | 0.71 | 0.84\n",
      "Global Step:   1460, accuracy (SELU/ELU/RELU):  78.9% |  78.1% |  68.0%, loss (SELU/ELU/RELU): 0.69 | 0.76 | 0.97\n",
      "Global Step:   1470, accuracy (SELU/ELU/RELU):  73.4% |  74.2% |  66.4%, loss (SELU/ELU/RELU): 0.77 | 0.76 | 0.92\n",
      "Global Step:   1480, accuracy (SELU/ELU/RELU):  71.1% |  76.6% |  61.7%, loss (SELU/ELU/RELU): 0.79 | 0.81 | 1.06\n",
      "Global Step:   1490, accuracy (SELU/ELU/RELU):  74.2% |  68.8% |  60.9%, loss (SELU/ELU/RELU): 0.74 | 0.77 | 1.02\n",
      "Global Step:   1500, accuracy (SELU/ELU/RELU):  75.0% |  67.2% |  63.3%, loss (SELU/ELU/RELU): 0.80 | 0.94 | 1.02\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 68.23% | 67.32% | 63.02%\n",
      "Saved checkpoint.\n",
      "Global Step:   1510, accuracy (SELU/ELU/RELU):  68.8% |  64.1% |  59.4%, loss (SELU/ELU/RELU): 0.87 | 0.93 | 1.12\n",
      "Global Step:   1520, accuracy (SELU/ELU/RELU):  75.0% |  71.1% |  62.5%, loss (SELU/ELU/RELU): 0.76 | 0.79 | 0.97\n",
      "Global Step:   1530, accuracy (SELU/ELU/RELU):  72.7% |  71.9% |  70.3%, loss (SELU/ELU/RELU): 0.68 | 0.74 | 0.91\n",
      "Global Step:   1540, accuracy (SELU/ELU/RELU):  65.6% |  68.8% |  64.1%, loss (SELU/ELU/RELU): 0.95 | 0.90 | 1.05\n",
      "Global Step:   1550, accuracy (SELU/ELU/RELU):  74.2% |  75.8% |  61.7%, loss (SELU/ELU/RELU): 0.72 | 0.76 | 0.99\n",
      "Global Step:   1560, accuracy (SELU/ELU/RELU):  78.9% |  74.2% |  65.6%, loss (SELU/ELU/RELU): 0.63 | 0.71 | 0.91\n",
      "Global Step:   1570, accuracy (SELU/ELU/RELU):  75.8% |  74.2% |  78.9%, loss (SELU/ELU/RELU): 0.65 | 0.67 | 0.73\n",
      "Global Step:   1580, accuracy (SELU/ELU/RELU):  68.8% |  68.0% |  60.9%, loss (SELU/ELU/RELU): 0.87 | 0.87 | 0.97\n",
      "Global Step:   1590, accuracy (SELU/ELU/RELU):  75.0% |  75.8% |  70.3%, loss (SELU/ELU/RELU): 0.67 | 0.72 | 0.85\n",
      "Global Step:   1600, accuracy (SELU/ELU/RELU):  71.1% |  71.1% |  65.6%, loss (SELU/ELU/RELU): 0.75 | 0.84 | 0.92\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 68.76% | 67.19% | 63.67%\n",
      "Saved checkpoint.\n",
      "Global Step:   1610, accuracy (SELU/ELU/RELU):  75.0% |  68.8% |  65.6%, loss (SELU/ELU/RELU): 0.80 | 0.86 | 1.00\n",
      "Global Step:   1620, accuracy (SELU/ELU/RELU):  70.3% |  67.2% |  59.4%, loss (SELU/ELU/RELU): 0.86 | 1.03 | 1.06\n",
      "Global Step:   1630, accuracy (SELU/ELU/RELU):  76.6% |  73.4% |  70.3%, loss (SELU/ELU/RELU): 0.78 | 0.81 | 0.94\n",
      "Global Step:   1640, accuracy (SELU/ELU/RELU):  75.8% |  72.7% |  62.5%, loss (SELU/ELU/RELU): 0.66 | 0.77 | 0.95\n",
      "Global Step:   1650, accuracy (SELU/ELU/RELU):  79.7% |  75.8% |  68.0%, loss (SELU/ELU/RELU): 0.57 | 0.61 | 0.81\n",
      "Global Step:   1660, accuracy (SELU/ELU/RELU):  82.0% |  78.9% |  70.3%, loss (SELU/ELU/RELU): 0.55 | 0.69 | 0.80\n",
      "Global Step:   1670, accuracy (SELU/ELU/RELU):  73.4% |  68.8% |  65.6%, loss (SELU/ELU/RELU): 0.78 | 0.86 | 0.98\n",
      "Global Step:   1680, accuracy (SELU/ELU/RELU):  74.2% |  69.5% |  69.5%, loss (SELU/ELU/RELU): 0.71 | 0.84 | 0.98\n",
      "Global Step:   1690, accuracy (SELU/ELU/RELU):  76.6% |  78.1% |  70.3%, loss (SELU/ELU/RELU): 0.65 | 0.68 | 0.83\n",
      "Global Step:   1700, accuracy (SELU/ELU/RELU):  65.6% |  63.3% |  57.8%, loss (SELU/ELU/RELU): 0.94 | 0.91 | 1.08\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 68.96% | 66.90% | 63.25%\n",
      "Saved checkpoint.\n",
      "Global Step:   1710, accuracy (SELU/ELU/RELU):  68.8% |  71.1% |  62.5%, loss (SELU/ELU/RELU): 0.79 | 0.78 | 0.94\n",
      "Global Step:   1720, accuracy (SELU/ELU/RELU):  71.9% |  70.3% |  67.2%, loss (SELU/ELU/RELU): 0.80 | 0.80 | 0.97\n",
      "Global Step:   1730, accuracy (SELU/ELU/RELU):  75.8% |  68.8% |  68.0%, loss (SELU/ELU/RELU): 0.65 | 0.74 | 0.78\n",
      "Global Step:   1740, accuracy (SELU/ELU/RELU):  68.8% |  72.7% |  64.1%, loss (SELU/ELU/RELU): 0.82 | 0.88 | 1.01\n",
      "Global Step:   1750, accuracy (SELU/ELU/RELU):  71.1% |  72.7% |  66.4%, loss (SELU/ELU/RELU): 0.72 | 0.76 | 0.94\n",
      "Global Step:   1760, accuracy (SELU/ELU/RELU):  68.0% |  66.4% |  64.8%, loss (SELU/ELU/RELU): 0.89 | 0.96 | 1.05\n",
      "Global Step:   1770, accuracy (SELU/ELU/RELU):  78.1% |  73.4% |  71.9%, loss (SELU/ELU/RELU): 0.57 | 0.63 | 0.76\n",
      "Global Step:   1780, accuracy (SELU/ELU/RELU):  79.7% |  75.0% |  71.1%, loss (SELU/ELU/RELU): 0.64 | 0.68 | 0.86\n",
      "Global Step:   1790, accuracy (SELU/ELU/RELU):  77.3% |  75.0% |  71.1%, loss (SELU/ELU/RELU): 0.71 | 0.80 | 0.99\n",
      "Global Step:   1800, accuracy (SELU/ELU/RELU):  77.3% |  74.2% |  73.4%, loss (SELU/ELU/RELU): 0.69 | 0.72 | 0.80\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 69.48% | 68.90% | 64.59%\n",
      "Saved checkpoint.\n",
      "Global Step:   1810, accuracy (SELU/ELU/RELU):  79.7% |  78.1% |  70.3%, loss (SELU/ELU/RELU): 0.68 | 0.74 | 0.91\n",
      "Global Step:   1820, accuracy (SELU/ELU/RELU):  73.4% |  70.3% |  68.8%, loss (SELU/ELU/RELU): 0.78 | 0.82 | 0.96\n",
      "Global Step:   1830, accuracy (SELU/ELU/RELU):  74.2% |  75.8% |  71.1%, loss (SELU/ELU/RELU): 0.68 | 0.72 | 0.86\n",
      "Global Step:   1840, accuracy (SELU/ELU/RELU):  79.7% |  73.4% |  74.2%, loss (SELU/ELU/RELU): 0.66 | 0.73 | 0.82\n",
      "Global Step:   1850, accuracy (SELU/ELU/RELU):  75.8% |  75.0% |  64.8%, loss (SELU/ELU/RELU): 0.69 | 0.73 | 0.90\n",
      "Global Step:   1860, accuracy (SELU/ELU/RELU):  72.7% |  75.0% |  66.4%, loss (SELU/ELU/RELU): 0.73 | 0.81 | 0.94\n",
      "Global Step:   1870, accuracy (SELU/ELU/RELU):  71.1% |  72.7% |  62.5%, loss (SELU/ELU/RELU): 0.85 | 0.82 | 1.01\n",
      "Global Step:   1880, accuracy (SELU/ELU/RELU):  78.1% |  72.7% |  68.8%, loss (SELU/ELU/RELU): 0.70 | 0.75 | 0.87\n",
      "Global Step:   1890, accuracy (SELU/ELU/RELU):  78.1% |  74.2% |  63.3%, loss (SELU/ELU/RELU): 0.57 | 0.66 | 0.80\n",
      "Global Step:   1900, accuracy (SELU/ELU/RELU):  74.2% |  72.7% |  68.0%, loss (SELU/ELU/RELU): 0.71 | 0.77 | 0.83\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 67.95% | 67.23% | 65.41%\n",
      "Saved checkpoint.\n",
      "Global Step:   1910, accuracy (SELU/ELU/RELU):  73.4% |  71.1% |  66.4%, loss (SELU/ELU/RELU): 0.77 | 0.80 | 0.96\n",
      "Global Step:   1920, accuracy (SELU/ELU/RELU):  78.1% |  75.0% |  65.6%, loss (SELU/ELU/RELU): 0.69 | 0.78 | 0.95\n",
      "Global Step:   1930, accuracy (SELU/ELU/RELU):  78.9% |  78.1% |  68.0%, loss (SELU/ELU/RELU): 0.61 | 0.73 | 0.91\n",
      "Global Step:   1940, accuracy (SELU/ELU/RELU):  78.9% |  79.7% |  69.5%, loss (SELU/ELU/RELU): 0.58 | 0.57 | 0.78\n",
      "Global Step:   1950, accuracy (SELU/ELU/RELU):  78.1% |  79.7% |  72.7%, loss (SELU/ELU/RELU): 0.66 | 0.64 | 0.82\n",
      "Global Step:   1960, accuracy (SELU/ELU/RELU):  75.8% |  74.2% |  70.3%, loss (SELU/ELU/RELU): 0.71 | 0.73 | 0.85\n",
      "Global Step:   1970, accuracy (SELU/ELU/RELU):  76.6% |  75.0% |  70.3%, loss (SELU/ELU/RELU): 0.76 | 0.80 | 0.92\n",
      "Global Step:   1980, accuracy (SELU/ELU/RELU):  82.8% |  74.2% |  70.3%, loss (SELU/ELU/RELU): 0.64 | 0.77 | 0.84\n",
      "Global Step:   1990, accuracy (SELU/ELU/RELU):  75.0% |  75.8% |  72.7%, loss (SELU/ELU/RELU): 0.68 | 0.70 | 0.88\n",
      "Global Step:   2000, accuracy (SELU/ELU/RELU):  78.9% |  78.1% |  75.8%, loss (SELU/ELU/RELU): 0.61 | 0.61 | 0.70\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 70.92% | 68.83% | 65.03%\n",
      "Saved checkpoint.\n",
      "Global Step:   2010, accuracy (SELU/ELU/RELU):  74.2% |  74.2% |  66.4%, loss (SELU/ELU/RELU): 0.76 | 0.72 | 0.91\n",
      "Global Step:   2020, accuracy (SELU/ELU/RELU):  70.3% |  70.3% |  66.4%, loss (SELU/ELU/RELU): 0.73 | 0.75 | 0.89\n",
      "Global Step:   2030, accuracy (SELU/ELU/RELU):  78.1% |  75.0% |  70.3%, loss (SELU/ELU/RELU): 0.67 | 0.72 | 0.85\n",
      "Global Step:   2040, accuracy (SELU/ELU/RELU):  67.2% |  64.8% |  61.7%, loss (SELU/ELU/RELU): 0.86 | 0.90 | 1.04\n",
      "Global Step:   2050, accuracy (SELU/ELU/RELU):  75.0% |  75.0% |  69.5%, loss (SELU/ELU/RELU): 0.74 | 0.73 | 0.84\n",
      "Global Step:   2060, accuracy (SELU/ELU/RELU):  71.9% |  77.3% |  71.1%, loss (SELU/ELU/RELU): 0.82 | 0.73 | 0.86\n",
      "Global Step:   2070, accuracy (SELU/ELU/RELU):  78.1% |  78.9% |  75.0%, loss (SELU/ELU/RELU): 0.65 | 0.64 | 0.71\n",
      "Global Step:   2080, accuracy (SELU/ELU/RELU):  75.8% |  67.2% |  65.6%, loss (SELU/ELU/RELU): 0.68 | 0.80 | 1.00\n",
      "Global Step:   2090, accuracy (SELU/ELU/RELU):  78.9% |  75.0% |  70.3%, loss (SELU/ELU/RELU): 0.61 | 0.66 | 0.81\n",
      "Global Step:   2100, accuracy (SELU/ELU/RELU):  74.2% |  74.2% |  73.4%, loss (SELU/ELU/RELU): 0.70 | 0.72 | 0.83\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 70.37% | 69.89% | 66.72%\n",
      "Saved checkpoint.\n",
      "Global Step:   2110, accuracy (SELU/ELU/RELU):  78.1% |  80.5% |  72.7%, loss (SELU/ELU/RELU): 0.58 | 0.64 | 0.77\n",
      "Global Step:   2120, accuracy (SELU/ELU/RELU):  79.7% |  82.8% |  68.8%, loss (SELU/ELU/RELU): 0.66 | 0.66 | 0.78\n",
      "Global Step:   2130, accuracy (SELU/ELU/RELU):  74.2% |  75.0% |  71.9%, loss (SELU/ELU/RELU): 0.68 | 0.71 | 0.82\n",
      "Global Step:   2140, accuracy (SELU/ELU/RELU):  78.1% |  77.3% |  71.9%, loss (SELU/ELU/RELU): 0.60 | 0.67 | 0.79\n",
      "Global Step:   2150, accuracy (SELU/ELU/RELU):  72.7% |  78.1% |  75.0%, loss (SELU/ELU/RELU): 0.79 | 0.74 | 0.86\n",
      "Global Step:   2160, accuracy (SELU/ELU/RELU):  78.1% |  76.6% |  73.4%, loss (SELU/ELU/RELU): 0.66 | 0.73 | 0.81\n",
      "Global Step:   2170, accuracy (SELU/ELU/RELU):  80.5% |  73.4% |  65.6%, loss (SELU/ELU/RELU): 0.62 | 0.72 | 0.90\n",
      "Global Step:   2180, accuracy (SELU/ELU/RELU):  77.3% |  70.3% |  67.2%, loss (SELU/ELU/RELU): 0.70 | 0.80 | 0.93\n",
      "Global Step:   2190, accuracy (SELU/ELU/RELU):  71.9% |  71.1% |  67.2%, loss (SELU/ELU/RELU): 0.76 | 0.85 | 0.94\n",
      "Global Step:   2200, accuracy (SELU/ELU/RELU):  70.3% |  70.3% |  62.5%, loss (SELU/ELU/RELU): 0.82 | 0.80 | 0.96\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 71.79% | 71.05% | 67.70%\n",
      "Saved checkpoint.\n",
      "Global Step:   2210, accuracy (SELU/ELU/RELU):  83.6% |  81.2% |  80.5%, loss (SELU/ELU/RELU): 0.52 | 0.54 | 0.60\n",
      "Global Step:   2220, accuracy (SELU/ELU/RELU):  78.1% |  71.9% |  71.9%, loss (SELU/ELU/RELU): 0.63 | 0.72 | 0.76\n",
      "Global Step:   2230, accuracy (SELU/ELU/RELU):  81.2% |  80.5% |  77.3%, loss (SELU/ELU/RELU): 0.64 | 0.67 | 0.81\n",
      "Global Step:   2240, accuracy (SELU/ELU/RELU):  75.8% |  77.3% |  71.9%, loss (SELU/ELU/RELU): 0.66 | 0.65 | 0.75\n",
      "Global Step:   2250, accuracy (SELU/ELU/RELU):  75.8% |  72.7% |  64.8%, loss (SELU/ELU/RELU): 0.79 | 0.84 | 0.96\n",
      "Global Step:   2260, accuracy (SELU/ELU/RELU):  77.3% |  75.8% |  68.0%, loss (SELU/ELU/RELU): 0.66 | 0.64 | 0.90\n",
      "Global Step:   2270, accuracy (SELU/ELU/RELU):  71.1% |  70.3% |  68.0%, loss (SELU/ELU/RELU): 0.81 | 0.85 | 0.89\n",
      "Global Step:   2280, accuracy (SELU/ELU/RELU):  74.2% |  73.4% |  71.1%, loss (SELU/ELU/RELU): 0.73 | 0.77 | 0.90\n",
      "Global Step:   2290, accuracy (SELU/ELU/RELU):  81.2% |  77.3% |  71.9%, loss (SELU/ELU/RELU): 0.66 | 0.76 | 0.93\n",
      "Global Step:   2300, accuracy (SELU/ELU/RELU):  80.5% |  78.1% |  71.9%, loss (SELU/ELU/RELU): 0.59 | 0.67 | 0.84\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 71.59% | 71.22% | 67.19%\n",
      "Saved checkpoint.\n",
      "Global Step:   2310, accuracy (SELU/ELU/RELU):  75.8% |  81.2% |  68.8%, loss (SELU/ELU/RELU): 0.68 | 0.69 | 0.82\n",
      "Global Step:   2320, accuracy (SELU/ELU/RELU):  79.7% |  75.8% |  68.8%, loss (SELU/ELU/RELU): 0.60 | 0.66 | 0.81\n",
      "Global Step:   2330, accuracy (SELU/ELU/RELU):  77.3% |  71.9% |  71.1%, loss (SELU/ELU/RELU): 0.64 | 0.73 | 0.80\n",
      "Global Step:   2340, accuracy (SELU/ELU/RELU):  81.2% |  80.5% |  70.3%, loss (SELU/ELU/RELU): 0.59 | 0.63 | 0.79\n",
      "Global Step:   2350, accuracy (SELU/ELU/RELU):  65.6% |  71.9% |  59.4%, loss (SELU/ELU/RELU): 0.76 | 0.77 | 1.03\n",
      "Global Step:   2360, accuracy (SELU/ELU/RELU):  75.8% |  75.0% |  64.8%, loss (SELU/ELU/RELU): 0.68 | 0.75 | 0.90\n",
      "Global Step:   2370, accuracy (SELU/ELU/RELU):  78.1% |  71.9% |  69.5%, loss (SELU/ELU/RELU): 0.63 | 0.72 | 0.84\n",
      "Global Step:   2380, accuracy (SELU/ELU/RELU):  78.9% |  83.6% |  71.9%, loss (SELU/ELU/RELU): 0.61 | 0.59 | 0.76\n",
      "Global Step:   2390, accuracy (SELU/ELU/RELU):  75.8% |  73.4% |  67.2%, loss (SELU/ELU/RELU): 0.74 | 0.77 | 0.89\n",
      "Global Step:   2400, accuracy (SELU/ELU/RELU):  83.6% |  74.2% |  74.2%, loss (SELU/ELU/RELU): 0.56 | 0.73 | 0.75\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 72.10% | 70.71% | 67.66%\n",
      "Saved checkpoint.\n",
      "Global Step:   2410, accuracy (SELU/ELU/RELU):  78.9% |  78.1% |  70.3%, loss (SELU/ELU/RELU): 0.53 | 0.59 | 0.77\n",
      "Global Step:   2420, accuracy (SELU/ELU/RELU):  77.3% |  78.9% |  70.3%, loss (SELU/ELU/RELU): 0.57 | 0.62 | 0.76\n",
      "Global Step:   2430, accuracy (SELU/ELU/RELU):  79.7% |  71.1% |  69.5%, loss (SELU/ELU/RELU): 0.70 | 0.80 | 0.92\n",
      "Global Step:   2440, accuracy (SELU/ELU/RELU):  78.9% |  79.7% |  79.7%, loss (SELU/ELU/RELU): 0.57 | 0.63 | 0.71\n",
      "Global Step:   2450, accuracy (SELU/ELU/RELU):  83.6% |  75.0% |  68.0%, loss (SELU/ELU/RELU): 0.54 | 0.67 | 0.88\n",
      "Global Step:   2460, accuracy (SELU/ELU/RELU):  81.2% |  71.1% |  71.1%, loss (SELU/ELU/RELU): 0.65 | 0.76 | 0.83\n",
      "Global Step:   2470, accuracy (SELU/ELU/RELU):  81.2% |  81.2% |  70.3%, loss (SELU/ELU/RELU): 0.51 | 0.60 | 0.78\n",
      "Global Step:   2480, accuracy (SELU/ELU/RELU):  78.9% |  78.1% |  74.2%, loss (SELU/ELU/RELU): 0.59 | 0.66 | 0.79\n",
      "Global Step:   2490, accuracy (SELU/ELU/RELU):  76.6% |  77.3% |  71.9%, loss (SELU/ELU/RELU): 0.60 | 0.65 | 0.87\n",
      "Global Step:   2500, accuracy (SELU/ELU/RELU):  71.1% |  77.3% |  71.1%, loss (SELU/ELU/RELU): 0.69 | 0.62 | 0.76\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 71.85% | 71.74% | 67.83%\n",
      "Saved checkpoint.\n",
      "Global Step:   2510, accuracy (SELU/ELU/RELU):  81.2% |  77.3% |  71.1%, loss (SELU/ELU/RELU): 0.59 | 0.66 | 0.77\n",
      "Global Step:   2520, accuracy (SELU/ELU/RELU):  80.5% |  82.0% |  74.2%, loss (SELU/ELU/RELU): 0.56 | 0.57 | 0.77\n",
      "Global Step:   2530, accuracy (SELU/ELU/RELU):  80.5% |  78.1% |  69.5%, loss (SELU/ELU/RELU): 0.61 | 0.65 | 0.83\n",
      "Global Step:   2540, accuracy (SELU/ELU/RELU):  78.9% |  74.2% |  70.3%, loss (SELU/ELU/RELU): 0.65 | 0.75 | 0.91\n",
      "Global Step:   2550, accuracy (SELU/ELU/RELU):  76.6% |  77.3% |  75.0%, loss (SELU/ELU/RELU): 0.64 | 0.67 | 0.86\n",
      "Global Step:   2560, accuracy (SELU/ELU/RELU):  80.5% |  76.6% |  74.2%, loss (SELU/ELU/RELU): 0.60 | 0.70 | 0.82\n",
      "Global Step:   2570, accuracy (SELU/ELU/RELU):  76.6% |  78.1% |  70.3%, loss (SELU/ELU/RELU): 0.58 | 0.64 | 0.85\n",
      "Global Step:   2580, accuracy (SELU/ELU/RELU):  82.0% |  75.8% |  75.0%, loss (SELU/ELU/RELU): 0.58 | 0.63 | 0.70\n",
      "Global Step:   2590, accuracy (SELU/ELU/RELU):  79.7% |  78.9% |  74.2%, loss (SELU/ELU/RELU): 0.60 | 0.59 | 0.74\n",
      "Global Step:   2600, accuracy (SELU/ELU/RELU):  78.9% |  77.3% |  70.3%, loss (SELU/ELU/RELU): 0.60 | 0.69 | 0.80\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 72.60% | 72.29% | 67.93%\n",
      "Saved checkpoint.\n",
      "Global Step:   2610, accuracy (SELU/ELU/RELU):  86.7% |  85.2% |  78.9%, loss (SELU/ELU/RELU): 0.38 | 0.45 | 0.56\n",
      "Global Step:   2620, accuracy (SELU/ELU/RELU):  82.0% |  76.6% |  78.1%, loss (SELU/ELU/RELU): 0.54 | 0.61 | 0.69\n",
      "Global Step:   2630, accuracy (SELU/ELU/RELU):  77.3% |  77.3% |  69.5%, loss (SELU/ELU/RELU): 0.67 | 0.76 | 0.91\n",
      "Global Step:   2640, accuracy (SELU/ELU/RELU):  80.5% |  79.7% |  75.8%, loss (SELU/ELU/RELU): 0.60 | 0.64 | 0.74\n",
      "Global Step:   2650, accuracy (SELU/ELU/RELU):  77.3% |  77.3% |  70.3%, loss (SELU/ELU/RELU): 0.62 | 0.68 | 0.87\n",
      "Global Step:   2660, accuracy (SELU/ELU/RELU):  84.4% |  78.9% |  76.6%, loss (SELU/ELU/RELU): 0.47 | 0.54 | 0.63\n",
      "Global Step:   2670, accuracy (SELU/ELU/RELU):  85.9% |  85.9% |  83.6%, loss (SELU/ELU/RELU): 0.45 | 0.49 | 0.59\n",
      "Global Step:   2680, accuracy (SELU/ELU/RELU):  84.4% |  78.9% |  80.5%, loss (SELU/ELU/RELU): 0.47 | 0.56 | 0.65\n",
      "Global Step:   2690, accuracy (SELU/ELU/RELU):  77.3% |  74.2% |  68.0%, loss (SELU/ELU/RELU): 0.65 | 0.72 | 0.86\n",
      "Global Step:   2700, accuracy (SELU/ELU/RELU):  75.0% |  70.3% |  64.1%, loss (SELU/ELU/RELU): 0.62 | 0.72 | 0.86\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 73.45% | 71.99% | 68.25%\n",
      "Saved checkpoint.\n",
      "Global Step:   2710, accuracy (SELU/ELU/RELU):  81.2% |  78.9% |  68.8%, loss (SELU/ELU/RELU): 0.58 | 0.59 | 0.83\n",
      "Global Step:   2720, accuracy (SELU/ELU/RELU):  82.8% |  78.9% |  72.7%, loss (SELU/ELU/RELU): 0.51 | 0.59 | 0.73\n",
      "Global Step:   2730, accuracy (SELU/ELU/RELU):  78.9% |  74.2% |  73.4%, loss (SELU/ELU/RELU): 0.66 | 0.71 | 0.75\n",
      "Global Step:   2740, accuracy (SELU/ELU/RELU):  81.2% |  81.2% |  71.9%, loss (SELU/ELU/RELU): 0.59 | 0.63 | 0.82\n",
      "Global Step:   2750, accuracy (SELU/ELU/RELU):  82.8% |  79.7% |  75.0%, loss (SELU/ELU/RELU): 0.50 | 0.55 | 0.75\n",
      "Global Step:   2760, accuracy (SELU/ELU/RELU):  82.8% |  81.2% |  76.6%, loss (SELU/ELU/RELU): 0.57 | 0.57 | 0.69\n",
      "Global Step:   2770, accuracy (SELU/ELU/RELU):  81.2% |  74.2% |  76.6%, loss (SELU/ELU/RELU): 0.53 | 0.62 | 0.67\n",
      "Global Step:   2780, accuracy (SELU/ELU/RELU):  78.1% |  74.2% |  73.4%, loss (SELU/ELU/RELU): 0.63 | 0.71 | 0.82\n",
      "Global Step:   2790, accuracy (SELU/ELU/RELU):  80.5% |  81.2% |  71.9%, loss (SELU/ELU/RELU): 0.59 | 0.62 | 0.80\n",
      "Global Step:   2800, accuracy (SELU/ELU/RELU):  82.8% |  80.5% |  74.2%, loss (SELU/ELU/RELU): 0.53 | 0.61 | 0.78\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 73.31% | 72.91% | 69.06%\n",
      "Saved checkpoint.\n",
      "Global Step:   2810, accuracy (SELU/ELU/RELU):  80.5% |  73.4% |  72.7%, loss (SELU/ELU/RELU): 0.58 | 0.66 | 0.83\n",
      "Global Step:   2820, accuracy (SELU/ELU/RELU):  78.9% |  74.2% |  71.9%, loss (SELU/ELU/RELU): 0.60 | 0.73 | 0.73\n",
      "Global Step:   2830, accuracy (SELU/ELU/RELU):  71.1% |  74.2% |  72.7%, loss (SELU/ELU/RELU): 0.75 | 0.75 | 0.75\n",
      "Global Step:   2840, accuracy (SELU/ELU/RELU):  76.6% |  78.1% |  74.2%, loss (SELU/ELU/RELU): 0.60 | 0.58 | 0.78\n",
      "Global Step:   2850, accuracy (SELU/ELU/RELU):  73.4% |  75.8% |  70.3%, loss (SELU/ELU/RELU): 0.67 | 0.72 | 0.91\n",
      "Global Step:   2860, accuracy (SELU/ELU/RELU):  82.0% |  82.0% |  76.6%, loss (SELU/ELU/RELU): 0.47 | 0.51 | 0.65\n",
      "Global Step:   2870, accuracy (SELU/ELU/RELU):  79.7% |  78.9% |  75.8%, loss (SELU/ELU/RELU): 0.56 | 0.57 | 0.75\n",
      "Global Step:   2880, accuracy (SELU/ELU/RELU):  80.5% |  82.0% |  71.1%, loss (SELU/ELU/RELU): 0.52 | 0.53 | 0.82\n",
      "Global Step:   2890, accuracy (SELU/ELU/RELU):  82.8% |  84.4% |  72.7%, loss (SELU/ELU/RELU): 0.48 | 0.50 | 0.71\n",
      "Global Step:   2900, accuracy (SELU/ELU/RELU):  78.9% |  73.4% |  73.4%, loss (SELU/ELU/RELU): 0.64 | 0.76 | 0.82\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 72.08% | 72.28% | 69.18%\n",
      "Saved checkpoint.\n",
      "Global Step:   2910, accuracy (SELU/ELU/RELU):  80.5% |  76.6% |  70.3%, loss (SELU/ELU/RELU): 0.58 | 0.62 | 0.83\n",
      "Global Step:   2920, accuracy (SELU/ELU/RELU):  82.8% |  77.3% |  72.7%, loss (SELU/ELU/RELU): 0.49 | 0.59 | 0.79\n",
      "Global Step:   2930, accuracy (SELU/ELU/RELU):  81.2% |  80.5% |  76.6%, loss (SELU/ELU/RELU): 0.59 | 0.58 | 0.74\n",
      "Global Step:   2940, accuracy (SELU/ELU/RELU):  76.6% |  77.3% |  74.2%, loss (SELU/ELU/RELU): 0.56 | 0.56 | 0.76\n",
      "Global Step:   2950, accuracy (SELU/ELU/RELU):  86.7% |  78.1% |  74.2%, loss (SELU/ELU/RELU): 0.48 | 0.60 | 0.76\n",
      "Global Step:   2960, accuracy (SELU/ELU/RELU):  85.9% |  80.5% |  82.8%, loss (SELU/ELU/RELU): 0.42 | 0.51 | 0.60\n",
      "Global Step:   2970, accuracy (SELU/ELU/RELU):  82.0% |  82.0% |  76.6%, loss (SELU/ELU/RELU): 0.67 | 0.72 | 0.85\n",
      "Global Step:   2980, accuracy (SELU/ELU/RELU):  78.9% |  77.3% |  71.9%, loss (SELU/ELU/RELU): 0.62 | 0.65 | 0.80\n",
      "Global Step:   2990, accuracy (SELU/ELU/RELU):  83.6% |  78.9% |  75.0%, loss (SELU/ELU/RELU): 0.50 | 0.57 | 0.73\n",
      "Global Step:   3000, accuracy (SELU/ELU/RELU):  82.0% |  75.0% |  76.6%, loss (SELU/ELU/RELU): 0.58 | 0.65 | 0.73\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 73.06% | 72.75% | 68.44%\n",
      "Saved checkpoint.\n",
      "Global Step:   3010, accuracy (SELU/ELU/RELU):  83.6% |  80.5% |  71.9%, loss (SELU/ELU/RELU): 0.51 | 0.52 | 0.67\n",
      "Global Step:   3020, accuracy (SELU/ELU/RELU):  82.0% |  78.9% |  78.1%, loss (SELU/ELU/RELU): 0.50 | 0.52 | 0.74\n",
      "Global Step:   3030, accuracy (SELU/ELU/RELU):  82.8% |  82.0% |  74.2%, loss (SELU/ELU/RELU): 0.49 | 0.49 | 0.79\n",
      "Global Step:   3040, accuracy (SELU/ELU/RELU):  82.0% |  79.7% |  74.2%, loss (SELU/ELU/RELU): 0.59 | 0.63 | 0.69\n",
      "Global Step:   3050, accuracy (SELU/ELU/RELU):  81.2% |  82.8% |  73.4%, loss (SELU/ELU/RELU): 0.53 | 0.59 | 0.78\n",
      "Global Step:   3060, accuracy (SELU/ELU/RELU):  79.7% |  78.9% |  78.1%, loss (SELU/ELU/RELU): 0.53 | 0.61 | 0.65\n",
      "Global Step:   3070, accuracy (SELU/ELU/RELU):  82.8% |  82.0% |  77.3%, loss (SELU/ELU/RELU): 0.53 | 0.54 | 0.70\n",
      "Global Step:   3080, accuracy (SELU/ELU/RELU):  82.0% |  79.7% |  77.3%, loss (SELU/ELU/RELU): 0.51 | 0.54 | 0.67\n",
      "Global Step:   3090, accuracy (SELU/ELU/RELU):  82.0% |  81.2% |  77.3%, loss (SELU/ELU/RELU): 0.49 | 0.55 | 0.73\n",
      "Global Step:   3100, accuracy (SELU/ELU/RELU):  82.8% |  83.6% |  73.4%, loss (SELU/ELU/RELU): 0.44 | 0.50 | 0.66\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 72.95% | 73.14% | 69.69%\n",
      "Saved checkpoint.\n",
      "Global Step:   3110, accuracy (SELU/ELU/RELU):  78.1% |  78.1% |  73.4%, loss (SELU/ELU/RELU): 0.52 | 0.59 | 0.67\n",
      "Global Step:   3120, accuracy (SELU/ELU/RELU):  84.4% |  76.6% |  75.0%, loss (SELU/ELU/RELU): 0.51 | 0.55 | 0.66\n",
      "Global Step:   3130, accuracy (SELU/ELU/RELU):  85.2% |  78.1% |  75.0%, loss (SELU/ELU/RELU): 0.43 | 0.49 | 0.62\n",
      "Global Step:   3140, accuracy (SELU/ELU/RELU):  83.6% |  77.3% |  69.5%, loss (SELU/ELU/RELU): 0.44 | 0.56 | 0.78\n",
      "Global Step:   3150, accuracy (SELU/ELU/RELU):  85.9% |  84.4% |  80.5%, loss (SELU/ELU/RELU): 0.42 | 0.48 | 0.61\n",
      "Global Step:   3160, accuracy (SELU/ELU/RELU):  80.5% |  80.5% |  76.6%, loss (SELU/ELU/RELU): 0.57 | 0.58 | 0.75\n",
      "Global Step:   3170, accuracy (SELU/ELU/RELU):  82.0% |  75.0% |  75.8%, loss (SELU/ELU/RELU): 0.50 | 0.58 | 0.70\n",
      "Global Step:   3180, accuracy (SELU/ELU/RELU):  84.4% |  80.5% |  74.2%, loss (SELU/ELU/RELU): 0.46 | 0.50 | 0.64\n",
      "Global Step:   3190, accuracy (SELU/ELU/RELU):  83.6% |  78.9% |  78.9%, loss (SELU/ELU/RELU): 0.52 | 0.58 | 0.68\n",
      "Global Step:   3200, accuracy (SELU/ELU/RELU):  78.1% |  80.5% |  71.9%, loss (SELU/ELU/RELU): 0.55 | 0.55 | 0.74\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 73.48% | 73.53% | 70.23%\n",
      "Saved checkpoint.\n",
      "Global Step:   3210, accuracy (SELU/ELU/RELU):  77.3% |  77.3% |  74.2%, loss (SELU/ELU/RELU): 0.57 | 0.63 | 0.73\n",
      "Global Step:   3220, accuracy (SELU/ELU/RELU):  82.8% |  84.4% |  77.3%, loss (SELU/ELU/RELU): 0.41 | 0.50 | 0.70\n",
      "Global Step:   3230, accuracy (SELU/ELU/RELU):  87.5% |  82.8% |  75.8%, loss (SELU/ELU/RELU): 0.43 | 0.46 | 0.61\n",
      "Global Step:   3240, accuracy (SELU/ELU/RELU):  82.8% |  75.0% |  68.0%, loss (SELU/ELU/RELU): 0.57 | 0.65 | 0.93\n",
      "Global Step:   3250, accuracy (SELU/ELU/RELU):  78.9% |  80.5% |  73.4%, loss (SELU/ELU/RELU): 0.57 | 0.54 | 0.72\n",
      "Global Step:   3260, accuracy (SELU/ELU/RELU):  78.1% |  74.2% |  71.9%, loss (SELU/ELU/RELU): 0.62 | 0.68 | 0.74\n",
      "Global Step:   3270, accuracy (SELU/ELU/RELU):  88.3% |  87.5% |  82.0%, loss (SELU/ELU/RELU): 0.41 | 0.38 | 0.55\n",
      "Global Step:   3280, accuracy (SELU/ELU/RELU):  85.2% |  78.9% |  78.9%, loss (SELU/ELU/RELU): 0.45 | 0.52 | 0.65\n",
      "Global Step:   3290, accuracy (SELU/ELU/RELU):  81.2% |  75.8% |  74.2%, loss (SELU/ELU/RELU): 0.68 | 0.73 | 0.82\n",
      "Global Step:   3300, accuracy (SELU/ELU/RELU):  88.3% |  78.1% |  77.3%, loss (SELU/ELU/RELU): 0.43 | 0.62 | 0.63\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.53% | 73.64% | 71.04%\n",
      "Saved checkpoint.\n",
      "Global Step:   3310, accuracy (SELU/ELU/RELU):  80.5% |  83.6% |  78.1%, loss (SELU/ELU/RELU): 0.44 | 0.48 | 0.57\n",
      "Global Step:   3320, accuracy (SELU/ELU/RELU):  84.4% |  83.6% |  79.7%, loss (SELU/ELU/RELU): 0.43 | 0.49 | 0.52\n",
      "Global Step:   3330, accuracy (SELU/ELU/RELU):  83.6% |  87.5% |  80.5%, loss (SELU/ELU/RELU): 0.48 | 0.46 | 0.67\n",
      "Global Step:   3340, accuracy (SELU/ELU/RELU):  86.7% |  87.5% |  81.2%, loss (SELU/ELU/RELU): 0.40 | 0.44 | 0.53\n",
      "Global Step:   3350, accuracy (SELU/ELU/RELU):  85.2% |  83.6% |  80.5%, loss (SELU/ELU/RELU): 0.44 | 0.46 | 0.58\n",
      "Global Step:   3360, accuracy (SELU/ELU/RELU):  85.2% |  82.8% |  78.9%, loss (SELU/ELU/RELU): 0.45 | 0.46 | 0.62\n",
      "Global Step:   3370, accuracy (SELU/ELU/RELU):  85.9% |  81.2% |  75.0%, loss (SELU/ELU/RELU): 0.44 | 0.54 | 0.77\n",
      "Global Step:   3380, accuracy (SELU/ELU/RELU):  78.1% |  75.0% |  73.4%, loss (SELU/ELU/RELU): 0.53 | 0.62 | 0.75\n",
      "Global Step:   3390, accuracy (SELU/ELU/RELU):  86.7% |  83.6% |  75.0%, loss (SELU/ELU/RELU): 0.39 | 0.44 | 0.68\n",
      "Global Step:   3400, accuracy (SELU/ELU/RELU):  86.7% |  85.2% |  77.3%, loss (SELU/ELU/RELU): 0.41 | 0.48 | 0.64\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.81% | 73.83% | 68.80%\n",
      "Saved checkpoint.\n",
      "Global Step:   3410, accuracy (SELU/ELU/RELU):  89.8% |  88.3% |  77.3%, loss (SELU/ELU/RELU): 0.34 | 0.38 | 0.61\n",
      "Global Step:   3420, accuracy (SELU/ELU/RELU):  88.3% |  86.7% |  77.3%, loss (SELU/ELU/RELU): 0.43 | 0.47 | 0.73\n",
      "Global Step:   3430, accuracy (SELU/ELU/RELU):  82.8% |  83.6% |  72.7%, loss (SELU/ELU/RELU): 0.50 | 0.52 | 0.77\n",
      "Global Step:   3440, accuracy (SELU/ELU/RELU):  87.5% |  84.4% |  78.9%, loss (SELU/ELU/RELU): 0.36 | 0.44 | 0.64\n",
      "Global Step:   3450, accuracy (SELU/ELU/RELU):  85.9% |  82.8% |  76.6%, loss (SELU/ELU/RELU): 0.44 | 0.52 | 0.67\n",
      "Global Step:   3460, accuracy (SELU/ELU/RELU):  82.0% |  76.6% |  73.4%, loss (SELU/ELU/RELU): 0.52 | 0.62 | 0.81\n",
      "Global Step:   3470, accuracy (SELU/ELU/RELU):  85.9% |  79.7% |  74.2%, loss (SELU/ELU/RELU): 0.48 | 0.57 | 0.78\n",
      "Global Step:   3480, accuracy (SELU/ELU/RELU):  85.2% |  82.8% |  75.8%, loss (SELU/ELU/RELU): 0.44 | 0.47 | 0.69\n",
      "Global Step:   3490, accuracy (SELU/ELU/RELU):  86.7% |  85.2% |  75.0%, loss (SELU/ELU/RELU): 0.44 | 0.50 | 0.72\n",
      "Global Step:   3500, accuracy (SELU/ELU/RELU):  85.9% |  83.6% |  78.9%, loss (SELU/ELU/RELU): 0.47 | 0.47 | 0.69\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.29% | 74.05% | 70.10%\n",
      "Saved checkpoint.\n",
      "Global Step:   3510, accuracy (SELU/ELU/RELU):  87.5% |  82.8% |  85.2%, loss (SELU/ELU/RELU): 0.40 | 0.45 | 0.56\n",
      "Global Step:   3520, accuracy (SELU/ELU/RELU):  78.1% |  78.1% |  75.8%, loss (SELU/ELU/RELU): 0.61 | 0.65 | 0.76\n",
      "Global Step:   3530, accuracy (SELU/ELU/RELU):  82.0% |  82.8% |  78.9%, loss (SELU/ELU/RELU): 0.54 | 0.53 | 0.69\n",
      "Global Step:   3540, accuracy (SELU/ELU/RELU):  84.4% |  79.7% |  76.6%, loss (SELU/ELU/RELU): 0.57 | 0.62 | 0.83\n",
      "Global Step:   3550, accuracy (SELU/ELU/RELU):  81.2% |  72.7% |  71.1%, loss (SELU/ELU/RELU): 0.55 | 0.62 | 0.83\n",
      "Global Step:   3560, accuracy (SELU/ELU/RELU):  86.7% |  82.0% |  75.8%, loss (SELU/ELU/RELU): 0.43 | 0.50 | 0.65\n",
      "Global Step:   3570, accuracy (SELU/ELU/RELU):  83.6% |  81.2% |  82.0%, loss (SELU/ELU/RELU): 0.41 | 0.45 | 0.51\n",
      "Global Step:   3580, accuracy (SELU/ELU/RELU):  82.8% |  83.6% |  76.6%, loss (SELU/ELU/RELU): 0.45 | 0.51 | 0.68\n",
      "Global Step:   3590, accuracy (SELU/ELU/RELU):  85.2% |  80.5% |  82.0%, loss (SELU/ELU/RELU): 0.43 | 0.49 | 0.62\n",
      "Global Step:   3600, accuracy (SELU/ELU/RELU):  84.4% |  82.0% |  77.3%, loss (SELU/ELU/RELU): 0.50 | 0.59 | 0.78\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.11% | 73.37% | 70.01%\n",
      "Saved checkpoint.\n",
      "Global Step:   3610, accuracy (SELU/ELU/RELU):  86.7% |  82.0% |  78.9%, loss (SELU/ELU/RELU): 0.49 | 0.55 | 0.68\n",
      "Global Step:   3620, accuracy (SELU/ELU/RELU):  78.9% |  82.8% |  73.4%, loss (SELU/ELU/RELU): 0.56 | 0.55 | 0.70\n",
      "Global Step:   3630, accuracy (SELU/ELU/RELU):  88.3% |  84.4% |  85.2%, loss (SELU/ELU/RELU): 0.41 | 0.49 | 0.56\n",
      "Global Step:   3640, accuracy (SELU/ELU/RELU):  89.8% |  89.8% |  84.4%, loss (SELU/ELU/RELU): 0.35 | 0.38 | 0.52\n",
      "Global Step:   3650, accuracy (SELU/ELU/RELU):  91.4% |  86.7% |  81.2%, loss (SELU/ELU/RELU): 0.32 | 0.40 | 0.58\n",
      "Global Step:   3660, accuracy (SELU/ELU/RELU):  83.6% |  80.5% |  79.7%, loss (SELU/ELU/RELU): 0.47 | 0.53 | 0.71\n",
      "Global Step:   3670, accuracy (SELU/ELU/RELU):  83.6% |  86.7% |  78.1%, loss (SELU/ELU/RELU): 0.49 | 0.47 | 0.65\n",
      "Global Step:   3680, accuracy (SELU/ELU/RELU):  84.4% |  85.2% |  78.1%, loss (SELU/ELU/RELU): 0.40 | 0.45 | 0.63\n",
      "Global Step:   3690, accuracy (SELU/ELU/RELU):  83.6% |  79.7% |  78.9%, loss (SELU/ELU/RELU): 0.47 | 0.55 | 0.59\n",
      "Global Step:   3700, accuracy (SELU/ELU/RELU):  86.7% |  85.9% |  82.8%, loss (SELU/ELU/RELU): 0.43 | 0.43 | 0.46\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.76% | 74.70% | 70.28%\n",
      "Saved checkpoint.\n",
      "Global Step:   3710, accuracy (SELU/ELU/RELU):  84.4% |  88.3% |  80.5%, loss (SELU/ELU/RELU): 0.44 | 0.40 | 0.54\n",
      "Global Step:   3720, accuracy (SELU/ELU/RELU):  88.3% |  85.9% |  78.9%, loss (SELU/ELU/RELU): 0.41 | 0.49 | 0.75\n",
      "Global Step:   3730, accuracy (SELU/ELU/RELU):  86.7% |  85.2% |  77.3%, loss (SELU/ELU/RELU): 0.39 | 0.42 | 0.61\n",
      "Global Step:   3740, accuracy (SELU/ELU/RELU):  88.3% |  87.5% |  77.3%, loss (SELU/ELU/RELU): 0.36 | 0.35 | 0.60\n",
      "Global Step:   3750, accuracy (SELU/ELU/RELU):  84.4% |  85.2% |  75.0%, loss (SELU/ELU/RELU): 0.44 | 0.41 | 0.60\n",
      "Global Step:   3760, accuracy (SELU/ELU/RELU):  90.6% |  85.2% |  81.2%, loss (SELU/ELU/RELU): 0.31 | 0.45 | 0.59\n",
      "Global Step:   3770, accuracy (SELU/ELU/RELU):  88.3% |  79.7% |  78.1%, loss (SELU/ELU/RELU): 0.32 | 0.42 | 0.56\n",
      "Global Step:   3780, accuracy (SELU/ELU/RELU):  82.0% |  80.5% |  84.4%, loss (SELU/ELU/RELU): 0.55 | 0.50 | 0.58\n",
      "Global Step:   3790, accuracy (SELU/ELU/RELU):  83.6% |  82.0% |  70.3%, loss (SELU/ELU/RELU): 0.46 | 0.51 | 0.66\n",
      "Global Step:   3800, accuracy (SELU/ELU/RELU):  83.6% |  86.7% |  82.0%, loss (SELU/ELU/RELU): 0.43 | 0.46 | 0.61\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.95% | 74.28% | 71.71%\n",
      "Saved checkpoint.\n",
      "Global Step:   3810, accuracy (SELU/ELU/RELU):  84.4% |  82.8% |  79.7%, loss (SELU/ELU/RELU): 0.49 | 0.49 | 0.60\n",
      "Global Step:   3820, accuracy (SELU/ELU/RELU):  92.2% |  89.1% |  85.2%, loss (SELU/ELU/RELU): 0.26 | 0.34 | 0.50\n",
      "Global Step:   3830, accuracy (SELU/ELU/RELU):  90.6% |  87.5% |  84.4%, loss (SELU/ELU/RELU): 0.37 | 0.42 | 0.55\n",
      "Global Step:   3840, accuracy (SELU/ELU/RELU):  85.9% |  86.7% |  78.1%, loss (SELU/ELU/RELU): 0.40 | 0.46 | 0.65\n",
      "Global Step:   3850, accuracy (SELU/ELU/RELU):  87.5% |  85.9% |  75.8%, loss (SELU/ELU/RELU): 0.40 | 0.39 | 0.58\n",
      "Global Step:   3860, accuracy (SELU/ELU/RELU):  85.9% |  85.9% |  74.2%, loss (SELU/ELU/RELU): 0.42 | 0.48 | 0.66\n",
      "Global Step:   3870, accuracy (SELU/ELU/RELU):  87.5% |  88.3% |  78.9%, loss (SELU/ELU/RELU): 0.42 | 0.40 | 0.64\n",
      "Global Step:   3880, accuracy (SELU/ELU/RELU):  85.9% |  81.2% |  73.4%, loss (SELU/ELU/RELU): 0.49 | 0.50 | 0.71\n",
      "Global Step:   3890, accuracy (SELU/ELU/RELU):  83.6% |  78.9% |  75.8%, loss (SELU/ELU/RELU): 0.48 | 0.59 | 0.68\n",
      "Global Step:   3900, accuracy (SELU/ELU/RELU):  85.2% |  87.5% |  78.9%, loss (SELU/ELU/RELU): 0.44 | 0.44 | 0.58\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.64% | 74.25% | 71.28%\n",
      "Saved checkpoint.\n",
      "Global Step:   3910, accuracy (SELU/ELU/RELU):  89.1% |  85.9% |  77.3%, loss (SELU/ELU/RELU): 0.34 | 0.38 | 0.57\n",
      "Global Step:   3920, accuracy (SELU/ELU/RELU):  90.6% |  88.3% |  83.6%, loss (SELU/ELU/RELU): 0.35 | 0.37 | 0.54\n",
      "Global Step:   3930, accuracy (SELU/ELU/RELU):  89.1% |  89.1% |  76.6%, loss (SELU/ELU/RELU): 0.31 | 0.32 | 0.61\n",
      "Global Step:   3940, accuracy (SELU/ELU/RELU):  89.1% |  86.7% |  76.6%, loss (SELU/ELU/RELU): 0.38 | 0.43 | 0.60\n",
      "Global Step:   3950, accuracy (SELU/ELU/RELU):  90.6% |  90.6% |  81.2%, loss (SELU/ELU/RELU): 0.31 | 0.35 | 0.60\n",
      "Global Step:   3960, accuracy (SELU/ELU/RELU):  89.8% |  88.3% |  82.8%, loss (SELU/ELU/RELU): 0.33 | 0.38 | 0.52\n",
      "Global Step:   3970, accuracy (SELU/ELU/RELU):  91.4% |  82.0% |  86.7%, loss (SELU/ELU/RELU): 0.38 | 0.44 | 0.55\n",
      "Global Step:   3980, accuracy (SELU/ELU/RELU):  85.2% |  85.9% |  80.5%, loss (SELU/ELU/RELU): 0.41 | 0.48 | 0.63\n",
      "Global Step:   3990, accuracy (SELU/ELU/RELU):  89.1% |  90.6% |  82.0%, loss (SELU/ELU/RELU): 0.34 | 0.41 | 0.53\n",
      "Global Step:   4000, accuracy (SELU/ELU/RELU):  91.4% |  88.3% |  82.0%, loss (SELU/ELU/RELU): 0.34 | 0.42 | 0.52\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.09% | 74.28% | 71.71%\n",
      "Saved checkpoint.\n",
      "Global Step:   4010, accuracy (SELU/ELU/RELU):  85.9% |  87.5% |  83.6%, loss (SELU/ELU/RELU): 0.47 | 0.50 | 0.52\n",
      "Global Step:   4020, accuracy (SELU/ELU/RELU):  85.9% |  85.9% |  81.2%, loss (SELU/ELU/RELU): 0.37 | 0.44 | 0.65\n",
      "Global Step:   4030, accuracy (SELU/ELU/RELU):  89.1% |  82.0% |  79.7%, loss (SELU/ELU/RELU): 0.36 | 0.39 | 0.58\n",
      "Global Step:   4040, accuracy (SELU/ELU/RELU):  85.9% |  85.2% |  80.5%, loss (SELU/ELU/RELU): 0.38 | 0.39 | 0.60\n",
      "Global Step:   4050, accuracy (SELU/ELU/RELU):  93.0% |  93.0% |  86.7%, loss (SELU/ELU/RELU): 0.26 | 0.27 | 0.44\n",
      "Global Step:   4060, accuracy (SELU/ELU/RELU):  87.5% |  86.7% |  80.5%, loss (SELU/ELU/RELU): 0.35 | 0.44 | 0.54\n",
      "Global Step:   4070, accuracy (SELU/ELU/RELU):  92.2% |  90.6% |  82.8%, loss (SELU/ELU/RELU): 0.26 | 0.30 | 0.54\n",
      "Global Step:   4080, accuracy (SELU/ELU/RELU):  86.7% |  84.4% |  76.6%, loss (SELU/ELU/RELU): 0.40 | 0.44 | 0.59\n",
      "Global Step:   4090, accuracy (SELU/ELU/RELU):  86.7% |  87.5% |  81.2%, loss (SELU/ELU/RELU): 0.38 | 0.44 | 0.55\n",
      "Global Step:   4100, accuracy (SELU/ELU/RELU):  86.7% |  82.8% |  75.8%, loss (SELU/ELU/RELU): 0.43 | 0.45 | 0.69\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.29% | 73.83% | 70.05%\n",
      "Saved checkpoint.\n",
      "Global Step:   4110, accuracy (SELU/ELU/RELU):  89.1% |  85.9% |  82.0%, loss (SELU/ELU/RELU): 0.34 | 0.40 | 0.51\n",
      "Global Step:   4120, accuracy (SELU/ELU/RELU):  90.6% |  89.1% |  80.5%, loss (SELU/ELU/RELU): 0.34 | 0.38 | 0.57\n",
      "Global Step:   4130, accuracy (SELU/ELU/RELU):  93.0% |  90.6% |  85.9%, loss (SELU/ELU/RELU): 0.25 | 0.31 | 0.40\n",
      "Global Step:   4140, accuracy (SELU/ELU/RELU):  86.7% |  84.4% |  85.2%, loss (SELU/ELU/RELU): 0.40 | 0.42 | 0.54\n",
      "Global Step:   4150, accuracy (SELU/ELU/RELU):  89.1% |  87.5% |  75.8%, loss (SELU/ELU/RELU): 0.40 | 0.45 | 0.61\n",
      "Global Step:   4160, accuracy (SELU/ELU/RELU):  85.9% |  78.9% |  78.9%, loss (SELU/ELU/RELU): 0.45 | 0.56 | 0.64\n",
      "Global Step:   4170, accuracy (SELU/ELU/RELU):  88.3% |  85.2% |  78.1%, loss (SELU/ELU/RELU): 0.35 | 0.40 | 0.61\n",
      "Global Step:   4180, accuracy (SELU/ELU/RELU):  90.6% |  83.6% |  77.3%, loss (SELU/ELU/RELU): 0.29 | 0.38 | 0.56\n",
      "Global Step:   4190, accuracy (SELU/ELU/RELU):  85.9% |  85.2% |  78.1%, loss (SELU/ELU/RELU): 0.37 | 0.41 | 0.70\n",
      "Global Step:   4200, accuracy (SELU/ELU/RELU):  90.6% |  89.8% |  82.8%, loss (SELU/ELU/RELU): 0.30 | 0.35 | 0.56\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.57% | 74.43% | 70.86%\n",
      "Saved checkpoint.\n",
      "Global Step:   4210, accuracy (SELU/ELU/RELU):  87.5% |  86.7% |  82.8%, loss (SELU/ELU/RELU): 0.29 | 0.33 | 0.48\n",
      "Global Step:   4220, accuracy (SELU/ELU/RELU):  84.4% |  86.7% |  82.8%, loss (SELU/ELU/RELU): 0.48 | 0.47 | 0.58\n",
      "Global Step:   4230, accuracy (SELU/ELU/RELU):  85.2% |  82.0% |  76.6%, loss (SELU/ELU/RELU): 0.47 | 0.49 | 0.61\n",
      "Global Step:   4240, accuracy (SELU/ELU/RELU):  81.2% |  75.8% |  71.1%, loss (SELU/ELU/RELU): 0.52 | 0.61 | 0.79\n",
      "Global Step:   4250, accuracy (SELU/ELU/RELU):  88.3% |  85.9% |  84.4%, loss (SELU/ELU/RELU): 0.36 | 0.36 | 0.54\n",
      "Global Step:   4260, accuracy (SELU/ELU/RELU):  89.8% |  84.4% |  80.5%, loss (SELU/ELU/RELU): 0.31 | 0.43 | 0.54\n",
      "Global Step:   4270, accuracy (SELU/ELU/RELU):  84.4% |  87.5% |  81.2%, loss (SELU/ELU/RELU): 0.38 | 0.36 | 0.54\n",
      "Global Step:   4280, accuracy (SELU/ELU/RELU):  90.6% |  87.5% |  85.9%, loss (SELU/ELU/RELU): 0.33 | 0.39 | 0.45\n",
      "Global Step:   4290, accuracy (SELU/ELU/RELU):  87.5% |  84.4% |  79.7%, loss (SELU/ELU/RELU): 0.43 | 0.44 | 0.55\n",
      "Global Step:   4300, accuracy (SELU/ELU/RELU):  89.8% |  91.4% |  88.3%, loss (SELU/ELU/RELU): 0.32 | 0.31 | 0.40\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.80% | 74.90% | 71.94%\n",
      "Saved checkpoint.\n",
      "Global Step:   4310, accuracy (SELU/ELU/RELU):  90.6% |  86.7% |  84.4%, loss (SELU/ELU/RELU): 0.36 | 0.39 | 0.56\n",
      "Global Step:   4320, accuracy (SELU/ELU/RELU):  88.3% |  85.9% |  81.2%, loss (SELU/ELU/RELU): 0.33 | 0.35 | 0.51\n",
      "Global Step:   4330, accuracy (SELU/ELU/RELU):  89.1% |  89.1% |  85.2%, loss (SELU/ELU/RELU): 0.26 | 0.34 | 0.48\n",
      "Global Step:   4340, accuracy (SELU/ELU/RELU):  83.6% |  85.2% |  74.2%, loss (SELU/ELU/RELU): 0.44 | 0.42 | 0.63\n",
      "Global Step:   4350, accuracy (SELU/ELU/RELU):  87.5% |  88.3% |  75.0%, loss (SELU/ELU/RELU): 0.36 | 0.34 | 0.62\n",
      "Global Step:   4360, accuracy (SELU/ELU/RELU):  90.6% |  83.6% |  82.8%, loss (SELU/ELU/RELU): 0.32 | 0.36 | 0.51\n",
      "Global Step:   4370, accuracy (SELU/ELU/RELU):  93.0% |  89.8% |  85.9%, loss (SELU/ELU/RELU): 0.24 | 0.29 | 0.41\n",
      "Global Step:   4380, accuracy (SELU/ELU/RELU):  90.6% |  87.5% |  84.4%, loss (SELU/ELU/RELU): 0.32 | 0.40 | 0.48\n",
      "Global Step:   4390, accuracy (SELU/ELU/RELU):  93.0% |  90.6% |  83.6%, loss (SELU/ELU/RELU): 0.25 | 0.31 | 0.44\n",
      "Global Step:   4400, accuracy (SELU/ELU/RELU):  87.5% |  85.9% |  87.5%, loss (SELU/ELU/RELU): 0.33 | 0.39 | 0.50\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.76% | 74.89% | 72.37%\n",
      "Saved checkpoint.\n",
      "Global Step:   4410, accuracy (SELU/ELU/RELU):  88.3% |  87.5% |  86.7%, loss (SELU/ELU/RELU): 0.33 | 0.41 | 0.50\n",
      "Global Step:   4420, accuracy (SELU/ELU/RELU):  84.4% |  82.8% |  82.8%, loss (SELU/ELU/RELU): 0.45 | 0.51 | 0.62\n",
      "Global Step:   4430, accuracy (SELU/ELU/RELU):  92.2% |  88.3% |  86.7%, loss (SELU/ELU/RELU): 0.31 | 0.34 | 0.60\n",
      "Global Step:   4440, accuracy (SELU/ELU/RELU):  93.0% |  87.5% |  82.0%, loss (SELU/ELU/RELU): 0.24 | 0.33 | 0.45\n",
      "Global Step:   4450, accuracy (SELU/ELU/RELU):  85.9% |  78.9% |  74.2%, loss (SELU/ELU/RELU): 0.38 | 0.45 | 0.66\n",
      "Global Step:   4460, accuracy (SELU/ELU/RELU):  89.1% |  89.8% |  78.1%, loss (SELU/ELU/RELU): 0.30 | 0.38 | 0.54\n",
      "Global Step:   4470, accuracy (SELU/ELU/RELU):  91.4% |  85.2% |  82.0%, loss (SELU/ELU/RELU): 0.27 | 0.45 | 0.60\n",
      "Global Step:   4480, accuracy (SELU/ELU/RELU):  88.3% |  86.7% |  81.2%, loss (SELU/ELU/RELU): 0.37 | 0.44 | 0.62\n",
      "Global Step:   4490, accuracy (SELU/ELU/RELU):  87.5% |  89.8% |  82.8%, loss (SELU/ELU/RELU): 0.34 | 0.35 | 0.53\n",
      "Global Step:   4500, accuracy (SELU/ELU/RELU):  89.1% |  92.2% |  86.7%, loss (SELU/ELU/RELU): 0.32 | 0.28 | 0.43\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.22% | 73.84% | 72.72%\n",
      "Saved checkpoint.\n",
      "Global Step:   4510, accuracy (SELU/ELU/RELU):  91.4% |  86.7% |  77.3%, loss (SELU/ELU/RELU): 0.26 | 0.37 | 0.59\n",
      "Global Step:   4520, accuracy (SELU/ELU/RELU):  87.5% |  83.6% |  77.3%, loss (SELU/ELU/RELU): 0.35 | 0.43 | 0.58\n",
      "Global Step:   4530, accuracy (SELU/ELU/RELU):  89.1% |  87.5% |  84.4%, loss (SELU/ELU/RELU): 0.30 | 0.33 | 0.48\n",
      "Global Step:   4540, accuracy (SELU/ELU/RELU):  88.3% |  90.6% |  86.7%, loss (SELU/ELU/RELU): 0.35 | 0.33 | 0.45\n",
      "Global Step:   4550, accuracy (SELU/ELU/RELU):  90.6% |  89.8% |  81.2%, loss (SELU/ELU/RELU): 0.29 | 0.35 | 0.51\n",
      "Global Step:   4560, accuracy (SELU/ELU/RELU):  88.3% |  93.0% |  79.7%, loss (SELU/ELU/RELU): 0.33 | 0.33 | 0.54\n",
      "Global Step:   4570, accuracy (SELU/ELU/RELU):  89.8% |  91.4% |  80.5%, loss (SELU/ELU/RELU): 0.24 | 0.28 | 0.47\n",
      "Global Step:   4580, accuracy (SELU/ELU/RELU):  93.0% |  89.8% |  84.4%, loss (SELU/ELU/RELU): 0.22 | 0.30 | 0.46\n",
      "Global Step:   4590, accuracy (SELU/ELU/RELU):  91.4% |  87.5% |  81.2%, loss (SELU/ELU/RELU): 0.32 | 0.35 | 0.55\n",
      "Global Step:   4600, accuracy (SELU/ELU/RELU):  91.4% |  87.5% |  87.5%, loss (SELU/ELU/RELU): 0.29 | 0.41 | 0.45\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.64% | 74.09% | 72.63%\n",
      "Saved checkpoint.\n",
      "Global Step:   4610, accuracy (SELU/ELU/RELU):  91.4% |  90.6% |  77.3%, loss (SELU/ELU/RELU): 0.28 | 0.30 | 0.59\n",
      "Global Step:   4620, accuracy (SELU/ELU/RELU):  93.0% |  89.1% |  85.2%, loss (SELU/ELU/RELU): 0.26 | 0.33 | 0.58\n",
      "Global Step:   4630, accuracy (SELU/ELU/RELU):  86.7% |  87.5% |  85.9%, loss (SELU/ELU/RELU): 0.32 | 0.32 | 0.40\n",
      "Global Step:   4640, accuracy (SELU/ELU/RELU):  91.4% |  93.8% |  85.2%, loss (SELU/ELU/RELU): 0.26 | 0.24 | 0.41\n",
      "Global Step:   4650, accuracy (SELU/ELU/RELU):  91.4% |  86.7% |  82.8%, loss (SELU/ELU/RELU): 0.27 | 0.36 | 0.47\n",
      "Global Step:   4660, accuracy (SELU/ELU/RELU):  88.3% |  83.6% |  79.7%, loss (SELU/ELU/RELU): 0.28 | 0.36 | 0.57\n",
      "Global Step:   4670, accuracy (SELU/ELU/RELU):  86.7% |  89.8% |  78.9%, loss (SELU/ELU/RELU): 0.38 | 0.38 | 0.56\n",
      "Global Step:   4680, accuracy (SELU/ELU/RELU):  89.1% |  87.5% |  78.9%, loss (SELU/ELU/RELU): 0.39 | 0.44 | 0.61\n",
      "Global Step:   4690, accuracy (SELU/ELU/RELU):  93.0% |  87.5% |  83.6%, loss (SELU/ELU/RELU): 0.28 | 0.34 | 0.48\n",
      "Global Step:   4700, accuracy (SELU/ELU/RELU):  92.2% |  85.2% |  84.4%, loss (SELU/ELU/RELU): 0.26 | 0.40 | 0.47\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.16% | 74.90% | 72.34%\n",
      "Saved checkpoint.\n",
      "Global Step:   4710, accuracy (SELU/ELU/RELU):  93.0% |  85.2% |  85.2%, loss (SELU/ELU/RELU): 0.30 | 0.42 | 0.50\n",
      "Global Step:   4720, accuracy (SELU/ELU/RELU):  92.2% |  89.1% |  85.2%, loss (SELU/ELU/RELU): 0.28 | 0.30 | 0.44\n",
      "Global Step:   4730, accuracy (SELU/ELU/RELU):  88.3% |  85.9% |  81.2%, loss (SELU/ELU/RELU): 0.31 | 0.34 | 0.54\n",
      "Global Step:   4740, accuracy (SELU/ELU/RELU):  89.8% |  91.4% |  79.7%, loss (SELU/ELU/RELU): 0.27 | 0.31 | 0.53\n",
      "Global Step:   4750, accuracy (SELU/ELU/RELU):  91.4% |  91.4% |  87.5%, loss (SELU/ELU/RELU): 0.21 | 0.25 | 0.44\n",
      "Global Step:   4760, accuracy (SELU/ELU/RELU):  90.6% |  87.5% |  78.1%, loss (SELU/ELU/RELU): 0.30 | 0.35 | 0.53\n",
      "Global Step:   4770, accuracy (SELU/ELU/RELU):  93.0% |  89.1% |  89.1%, loss (SELU/ELU/RELU): 0.30 | 0.35 | 0.47\n",
      "Global Step:   4780, accuracy (SELU/ELU/RELU):  89.1% |  90.6% |  77.3%, loss (SELU/ELU/RELU): 0.34 | 0.35 | 0.57\n",
      "Global Step:   4790, accuracy (SELU/ELU/RELU):  91.4% |  83.6% |  71.1%, loss (SELU/ELU/RELU): 0.26 | 0.40 | 0.76\n",
      "Global Step:   4800, accuracy (SELU/ELU/RELU):  90.6% |  82.8% |  81.2%, loss (SELU/ELU/RELU): 0.31 | 0.44 | 0.51\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.01% | 74.34% | 71.84%\n",
      "Saved checkpoint.\n",
      "Global Step:   4810, accuracy (SELU/ELU/RELU):  88.3% |  84.4% |  81.2%, loss (SELU/ELU/RELU): 0.32 | 0.44 | 0.54\n",
      "Global Step:   4820, accuracy (SELU/ELU/RELU):  92.2% |  90.6% |  83.6%, loss (SELU/ELU/RELU): 0.25 | 0.31 | 0.44\n",
      "Global Step:   4830, accuracy (SELU/ELU/RELU):  88.3% |  87.5% |  80.5%, loss (SELU/ELU/RELU): 0.36 | 0.36 | 0.58\n",
      "Global Step:   4840, accuracy (SELU/ELU/RELU):  93.8% |  88.3% |  85.2%, loss (SELU/ELU/RELU): 0.25 | 0.40 | 0.47\n",
      "Global Step:   4850, accuracy (SELU/ELU/RELU):  90.6% |  83.6% |  83.6%, loss (SELU/ELU/RELU): 0.30 | 0.39 | 0.53\n",
      "Global Step:   4860, accuracy (SELU/ELU/RELU):  78.9% |  86.7% |  78.1%, loss (SELU/ELU/RELU): 0.45 | 0.43 | 0.60\n",
      "Global Step:   4870, accuracy (SELU/ELU/RELU):  91.4% |  91.4% |  82.0%, loss (SELU/ELU/RELU): 0.31 | 0.29 | 0.47\n",
      "Global Step:   4880, accuracy (SELU/ELU/RELU):  93.0% |  89.1% |  83.6%, loss (SELU/ELU/RELU): 0.21 | 0.27 | 0.40\n",
      "Global Step:   4890, accuracy (SELU/ELU/RELU):  93.0% |  86.7% |  81.2%, loss (SELU/ELU/RELU): 0.31 | 0.36 | 0.53\n",
      "Global Step:   4900, accuracy (SELU/ELU/RELU):  86.7% |  86.7% |  77.3%, loss (SELU/ELU/RELU): 0.33 | 0.38 | 0.60\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.69% | 75.72% | 72.42%\n",
      "Saved checkpoint.\n",
      "Global Step:   4910, accuracy (SELU/ELU/RELU):  85.9% |  84.4% |  83.6%, loss (SELU/ELU/RELU): 0.33 | 0.41 | 0.50\n",
      "Global Step:   4920, accuracy (SELU/ELU/RELU):  93.0% |  92.2% |  89.1%, loss (SELU/ELU/RELU): 0.26 | 0.26 | 0.40\n",
      "Global Step:   4930, accuracy (SELU/ELU/RELU):  86.7% |  87.5% |  76.6%, loss (SELU/ELU/RELU): 0.36 | 0.34 | 0.52\n",
      "Global Step:   4940, accuracy (SELU/ELU/RELU):  89.1% |  90.6% |  79.7%, loss (SELU/ELU/RELU): 0.32 | 0.40 | 0.54\n",
      "Global Step:   4950, accuracy (SELU/ELU/RELU):  93.8% |  91.4% |  89.8%, loss (SELU/ELU/RELU): 0.24 | 0.28 | 0.32\n",
      "Global Step:   4960, accuracy (SELU/ELU/RELU):  89.8% |  87.5% |  79.7%, loss (SELU/ELU/RELU): 0.28 | 0.37 | 0.54\n",
      "Global Step:   4970, accuracy (SELU/ELU/RELU):  93.0% |  89.8% |  88.3%, loss (SELU/ELU/RELU): 0.27 | 0.25 | 0.39\n",
      "Global Step:   4980, accuracy (SELU/ELU/RELU):  88.3% |  86.7% |  79.7%, loss (SELU/ELU/RELU): 0.36 | 0.38 | 0.64\n",
      "Global Step:   4990, accuracy (SELU/ELU/RELU):  95.3% |  89.8% |  82.0%, loss (SELU/ELU/RELU): 0.20 | 0.27 | 0.47\n",
      "Global Step:   5000, accuracy (SELU/ELU/RELU):  90.6% |  88.3% |  86.7%, loss (SELU/ELU/RELU): 0.24 | 0.32 | 0.39\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.78% | 75.34% | 72.82%\n",
      "Saved checkpoint.\n",
      "Global Step:   5010, accuracy (SELU/ELU/RELU):  86.7% |  90.6% |  81.2%, loss (SELU/ELU/RELU): 0.30 | 0.36 | 0.55\n",
      "Global Step:   5020, accuracy (SELU/ELU/RELU):  91.4% |  92.2% |  79.7%, loss (SELU/ELU/RELU): 0.27 | 0.30 | 0.52\n",
      "Global Step:   5030, accuracy (SELU/ELU/RELU):  89.8% |  93.0% |  79.7%, loss (SELU/ELU/RELU): 0.28 | 0.34 | 0.53\n",
      "Global Step:   5040, accuracy (SELU/ELU/RELU):  96.9% |  92.2% |  82.0%, loss (SELU/ELU/RELU): 0.21 | 0.27 | 0.52\n",
      "Global Step:   5050, accuracy (SELU/ELU/RELU):  89.1% |  89.8% |  84.4%, loss (SELU/ELU/RELU): 0.31 | 0.36 | 0.51\n",
      "Global Step:   5060, accuracy (SELU/ELU/RELU):  90.6% |  88.3% |  86.7%, loss (SELU/ELU/RELU): 0.26 | 0.33 | 0.39\n",
      "Global Step:   5070, accuracy (SELU/ELU/RELU):  89.1% |  90.6% |  84.4%, loss (SELU/ELU/RELU): 0.30 | 0.33 | 0.49\n",
      "Global Step:   5080, accuracy (SELU/ELU/RELU):  89.8% |  92.2% |  81.2%, loss (SELU/ELU/RELU): 0.30 | 0.27 | 0.46\n",
      "Global Step:   5090, accuracy (SELU/ELU/RELU):  96.1% |  92.2% |  86.7%, loss (SELU/ELU/RELU): 0.17 | 0.25 | 0.38\n",
      "Global Step:   5100, accuracy (SELU/ELU/RELU):  92.2% |  90.6% |  85.9%, loss (SELU/ELU/RELU): 0.21 | 0.32 | 0.49\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.78% | 74.87% | 73.12%\n",
      "Saved checkpoint.\n",
      "Global Step:   5110, accuracy (SELU/ELU/RELU):  93.8% |  89.8% |  85.2%, loss (SELU/ELU/RELU): 0.24 | 0.30 | 0.46\n",
      "Global Step:   5120, accuracy (SELU/ELU/RELU):  92.2% |  92.2% |  85.2%, loss (SELU/ELU/RELU): 0.32 | 0.29 | 0.58\n",
      "Global Step:   5130, accuracy (SELU/ELU/RELU):  92.2% |  91.4% |  79.7%, loss (SELU/ELU/RELU): 0.22 | 0.24 | 0.51\n",
      "Global Step:   5140, accuracy (SELU/ELU/RELU):  94.5% |  88.3% |  85.9%, loss (SELU/ELU/RELU): 0.23 | 0.30 | 0.45\n",
      "Global Step:   5150, accuracy (SELU/ELU/RELU):  86.7% |  85.9% |  82.0%, loss (SELU/ELU/RELU): 0.36 | 0.41 | 0.48\n",
      "Global Step:   5160, accuracy (SELU/ELU/RELU):  91.4% |  86.7% |  79.7%, loss (SELU/ELU/RELU): 0.32 | 0.45 | 0.63\n",
      "Global Step:   5170, accuracy (SELU/ELU/RELU):  89.1% |  89.8% |  78.9%, loss (SELU/ELU/RELU): 0.28 | 0.31 | 0.56\n",
      "Global Step:   5180, accuracy (SELU/ELU/RELU):  96.9% |  94.5% |  88.3%, loss (SELU/ELU/RELU): 0.12 | 0.20 | 0.42\n",
      "Global Step:   5190, accuracy (SELU/ELU/RELU):  95.3% |  92.2% |  86.7%, loss (SELU/ELU/RELU): 0.20 | 0.30 | 0.44\n",
      "Global Step:   5200, accuracy (SELU/ELU/RELU):  91.4% |  89.8% |  83.6%, loss (SELU/ELU/RELU): 0.23 | 0.27 | 0.38\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.80% | 74.77% | 72.48%\n",
      "Saved checkpoint.\n",
      "Global Step:   5210, accuracy (SELU/ELU/RELU):  90.6% |  91.4% |  87.5%, loss (SELU/ELU/RELU): 0.27 | 0.33 | 0.44\n",
      "Global Step:   5220, accuracy (SELU/ELU/RELU):  93.8% |  92.2% |  93.0%, loss (SELU/ELU/RELU): 0.21 | 0.19 | 0.31\n",
      "Global Step:   5230, accuracy (SELU/ELU/RELU):  90.6% |  89.1% |  83.6%, loss (SELU/ELU/RELU): 0.30 | 0.35 | 0.55\n",
      "Global Step:   5240, accuracy (SELU/ELU/RELU):  90.6% |  93.0% |  86.7%, loss (SELU/ELU/RELU): 0.30 | 0.24 | 0.40\n",
      "Global Step:   5250, accuracy (SELU/ELU/RELU):  93.0% |  91.4% |  82.0%, loss (SELU/ELU/RELU): 0.23 | 0.23 | 0.45\n",
      "Global Step:   5260, accuracy (SELU/ELU/RELU):  89.8% |  89.8% |  81.2%, loss (SELU/ELU/RELU): 0.30 | 0.29 | 0.48\n",
      "Global Step:   5270, accuracy (SELU/ELU/RELU):  95.3% |  93.8% |  83.6%, loss (SELU/ELU/RELU): 0.24 | 0.23 | 0.55\n",
      "Global Step:   5280, accuracy (SELU/ELU/RELU):  93.0% |  89.8% |  85.2%, loss (SELU/ELU/RELU): 0.19 | 0.27 | 0.44\n",
      "Global Step:   5290, accuracy (SELU/ELU/RELU):  96.1% |  91.4% |  86.7%, loss (SELU/ELU/RELU): 0.15 | 0.27 | 0.33\n",
      "Global Step:   5300, accuracy (SELU/ELU/RELU):  87.5% |  86.7% |  81.2%, loss (SELU/ELU/RELU): 0.35 | 0.39 | 0.54\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.89% | 74.91% | 73.72%\n",
      "Saved checkpoint.\n",
      "Global Step:   5310, accuracy (SELU/ELU/RELU):  87.5% |  86.7% |  79.7%, loss (SELU/ELU/RELU): 0.33 | 0.41 | 0.56\n",
      "Global Step:   5320, accuracy (SELU/ELU/RELU):  93.8% |  93.0% |  84.4%, loss (SELU/ELU/RELU): 0.19 | 0.25 | 0.41\n",
      "Global Step:   5330, accuracy (SELU/ELU/RELU):  93.8% |  89.8% |  93.0%, loss (SELU/ELU/RELU): 0.21 | 0.25 | 0.34\n",
      "Global Step:   5340, accuracy (SELU/ELU/RELU):  93.8% |  91.4% |  85.9%, loss (SELU/ELU/RELU): 0.21 | 0.27 | 0.44\n",
      "Global Step:   5350, accuracy (SELU/ELU/RELU):  93.0% |  93.8% |  85.2%, loss (SELU/ELU/RELU): 0.26 | 0.26 | 0.49\n",
      "Global Step:   5360, accuracy (SELU/ELU/RELU):  96.1% |  96.9% |  85.2%, loss (SELU/ELU/RELU): 0.13 | 0.14 | 0.33\n",
      "Global Step:   5370, accuracy (SELU/ELU/RELU):  93.8% |  88.3% |  82.8%, loss (SELU/ELU/RELU): 0.26 | 0.42 | 0.57\n",
      "Global Step:   5380, accuracy (SELU/ELU/RELU):  94.5% |  93.8% |  84.4%, loss (SELU/ELU/RELU): 0.18 | 0.24 | 0.39\n",
      "Global Step:   5390, accuracy (SELU/ELU/RELU):  96.1% |  92.2% |  89.8%, loss (SELU/ELU/RELU): 0.19 | 0.23 | 0.35\n",
      "Global Step:   5400, accuracy (SELU/ELU/RELU):  91.4% |  89.8% |  89.1%, loss (SELU/ELU/RELU): 0.26 | 0.30 | 0.39\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.10% | 75.50% | 73.83%\n",
      "Saved checkpoint.\n",
      "Global Step:   5410, accuracy (SELU/ELU/RELU):  93.0% |  91.4% |  82.8%, loss (SELU/ELU/RELU): 0.26 | 0.30 | 0.44\n",
      "Global Step:   5420, accuracy (SELU/ELU/RELU):  94.5% |  89.1% |  85.2%, loss (SELU/ELU/RELU): 0.20 | 0.28 | 0.40\n",
      "Global Step:   5430, accuracy (SELU/ELU/RELU):  92.2% |  90.6% |  85.9%, loss (SELU/ELU/RELU): 0.24 | 0.29 | 0.43\n",
      "Global Step:   5440, accuracy (SELU/ELU/RELU):  93.0% |  89.8% |  83.6%, loss (SELU/ELU/RELU): 0.22 | 0.28 | 0.49\n",
      "Global Step:   5450, accuracy (SELU/ELU/RELU):  96.1% |  95.3% |  89.8%, loss (SELU/ELU/RELU): 0.17 | 0.20 | 0.29\n",
      "Global Step:   5460, accuracy (SELU/ELU/RELU):  96.1% |  90.6% |  84.4%, loss (SELU/ELU/RELU): 0.18 | 0.28 | 0.45\n",
      "Global Step:   5470, accuracy (SELU/ELU/RELU):  91.4% |  93.0% |  84.4%, loss (SELU/ELU/RELU): 0.20 | 0.24 | 0.42\n",
      "Global Step:   5480, accuracy (SELU/ELU/RELU):  93.0% |  95.3% |  85.9%, loss (SELU/ELU/RELU): 0.19 | 0.23 | 0.38\n",
      "Global Step:   5490, accuracy (SELU/ELU/RELU):  91.4% |  83.6% |  81.2%, loss (SELU/ELU/RELU): 0.33 | 0.40 | 0.53\n",
      "Global Step:   5500, accuracy (SELU/ELU/RELU):  93.0% |  89.8% |  86.7%, loss (SELU/ELU/RELU): 0.17 | 0.27 | 0.35\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.10% | 75.27% | 73.32%\n",
      "Saved checkpoint.\n",
      "Global Step:   5510, accuracy (SELU/ELU/RELU):  97.7% |  96.1% |  91.4%, loss (SELU/ELU/RELU): 0.13 | 0.13 | 0.29\n",
      "Global Step:   5520, accuracy (SELU/ELU/RELU):  98.4% |  93.0% |  91.4%, loss (SELU/ELU/RELU): 0.16 | 0.22 | 0.32\n",
      "Global Step:   5530, accuracy (SELU/ELU/RELU):  94.5% |  93.0% |  89.1%, loss (SELU/ELU/RELU): 0.15 | 0.22 | 0.37\n",
      "Global Step:   5540, accuracy (SELU/ELU/RELU):  93.8% |  91.4% |  86.7%, loss (SELU/ELU/RELU): 0.19 | 0.24 | 0.41\n",
      "Global Step:   5550, accuracy (SELU/ELU/RELU):  94.5% |  92.2% |  85.9%, loss (SELU/ELU/RELU): 0.17 | 0.25 | 0.43\n",
      "Global Step:   5560, accuracy (SELU/ELU/RELU):  93.0% |  96.1% |  82.8%, loss (SELU/ELU/RELU): 0.20 | 0.19 | 0.42\n",
      "Global Step:   5570, accuracy (SELU/ELU/RELU):  93.8% |  90.6% |  86.7%, loss (SELU/ELU/RELU): 0.24 | 0.31 | 0.43\n",
      "Global Step:   5580, accuracy (SELU/ELU/RELU):  96.9% |  95.3% |  85.9%, loss (SELU/ELU/RELU): 0.13 | 0.20 | 0.36\n",
      "Global Step:   5590, accuracy (SELU/ELU/RELU):  95.3% |  91.4% |  86.7%, loss (SELU/ELU/RELU): 0.14 | 0.21 | 0.33\n",
      "Global Step:   5600, accuracy (SELU/ELU/RELU):  91.4% |  92.2% |  88.3%, loss (SELU/ELU/RELU): 0.20 | 0.24 | 0.38\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.40% | 75.60% | 74.05%\n",
      "Saved checkpoint.\n",
      "Global Step:   5610, accuracy (SELU/ELU/RELU):  93.0% |  89.8% |  85.2%, loss (SELU/ELU/RELU): 0.26 | 0.28 | 0.46\n",
      "Global Step:   5620, accuracy (SELU/ELU/RELU):  93.0% |  93.0% |  85.9%, loss (SELU/ELU/RELU): 0.23 | 0.23 | 0.45\n",
      "Global Step:   5630, accuracy (SELU/ELU/RELU):  93.8% |  93.8% |  85.2%, loss (SELU/ELU/RELU): 0.23 | 0.22 | 0.43\n",
      "Global Step:   5640, accuracy (SELU/ELU/RELU):  94.5% |  92.2% |  83.6%, loss (SELU/ELU/RELU): 0.18 | 0.26 | 0.56\n",
      "Global Step:   5650, accuracy (SELU/ELU/RELU):  93.8% |  95.3% |  87.5%, loss (SELU/ELU/RELU): 0.16 | 0.19 | 0.37\n",
      "Global Step:   5660, accuracy (SELU/ELU/RELU):  92.2% |  90.6% |  79.7%, loss (SELU/ELU/RELU): 0.25 | 0.31 | 0.52\n",
      "Global Step:   5670, accuracy (SELU/ELU/RELU):  95.3% |  93.8% |  88.3%, loss (SELU/ELU/RELU): 0.22 | 0.21 | 0.44\n",
      "Global Step:   5680, accuracy (SELU/ELU/RELU):  92.2% |  92.2% |  89.1%, loss (SELU/ELU/RELU): 0.20 | 0.21 | 0.35\n",
      "Global Step:   5690, accuracy (SELU/ELU/RELU):  95.3% |  95.3% |  89.1%, loss (SELU/ELU/RELU): 0.14 | 0.19 | 0.35\n",
      "Global Step:   5700, accuracy (SELU/ELU/RELU):  96.9% |  93.8% |  85.2%, loss (SELU/ELU/RELU): 0.17 | 0.19 | 0.35\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.69% | 76.01% | 73.29%\n",
      "Saved checkpoint.\n",
      "Global Step:   5710, accuracy (SELU/ELU/RELU):  97.7% |  93.0% |  85.2%, loss (SELU/ELU/RELU): 0.17 | 0.25 | 0.37\n",
      "Global Step:   5720, accuracy (SELU/ELU/RELU):  96.9% |  96.1% |  95.3%, loss (SELU/ELU/RELU): 0.11 | 0.15 | 0.26\n",
      "Global Step:   5730, accuracy (SELU/ELU/RELU):  97.7% |  93.0% |  85.9%, loss (SELU/ELU/RELU): 0.13 | 0.24 | 0.41\n",
      "Global Step:   5740, accuracy (SELU/ELU/RELU):  91.4% |  89.8% |  82.8%, loss (SELU/ELU/RELU): 0.26 | 0.29 | 0.44\n",
      "Global Step:   5750, accuracy (SELU/ELU/RELU):  93.8% |  93.8% |  88.3%, loss (SELU/ELU/RELU): 0.21 | 0.24 | 0.40\n",
      "Global Step:   5760, accuracy (SELU/ELU/RELU):  94.5% |  89.8% |  86.7%, loss (SELU/ELU/RELU): 0.18 | 0.25 | 0.39\n",
      "Global Step:   5770, accuracy (SELU/ELU/RELU):  90.6% |  94.5% |  83.6%, loss (SELU/ELU/RELU): 0.26 | 0.18 | 0.45\n",
      "Global Step:   5780, accuracy (SELU/ELU/RELU):  94.5% |  92.2% |  85.2%, loss (SELU/ELU/RELU): 0.13 | 0.21 | 0.35\n",
      "Global Step:   5790, accuracy (SELU/ELU/RELU):  93.0% |  93.0% |  85.2%, loss (SELU/ELU/RELU): 0.16 | 0.23 | 0.36\n",
      "Global Step:   5800, accuracy (SELU/ELU/RELU):  93.0% |  89.8% |  89.8%, loss (SELU/ELU/RELU): 0.24 | 0.24 | 0.36\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.87% | 75.86% | 74.21%\n",
      "Saved checkpoint.\n",
      "Global Step:   5810, accuracy (SELU/ELU/RELU):  93.0% |  89.8% |  89.1%, loss (SELU/ELU/RELU): 0.20 | 0.22 | 0.43\n",
      "Global Step:   5820, accuracy (SELU/ELU/RELU):  95.3% |  94.5% |  88.3%, loss (SELU/ELU/RELU): 0.16 | 0.20 | 0.34\n",
      "Global Step:   5830, accuracy (SELU/ELU/RELU):  94.5% |  93.0% |  89.1%, loss (SELU/ELU/RELU): 0.16 | 0.20 | 0.33\n",
      "Global Step:   5840, accuracy (SELU/ELU/RELU):  93.8% |  93.0% |  88.3%, loss (SELU/ELU/RELU): 0.20 | 0.24 | 0.41\n",
      "Global Step:   5850, accuracy (SELU/ELU/RELU):  96.1% |  92.2% |  89.8%, loss (SELU/ELU/RELU): 0.23 | 0.24 | 0.38\n",
      "Global Step:   5860, accuracy (SELU/ELU/RELU):  95.3% |  94.5% |  90.6%, loss (SELU/ELU/RELU): 0.20 | 0.25 | 0.34\n",
      "Global Step:   5870, accuracy (SELU/ELU/RELU):  96.9% |  93.8% |  85.2%, loss (SELU/ELU/RELU): 0.17 | 0.21 | 0.41\n",
      "Global Step:   5880, accuracy (SELU/ELU/RELU):  92.2% |  90.6% |  89.1%, loss (SELU/ELU/RELU): 0.19 | 0.23 | 0.37\n",
      "Global Step:   5890, accuracy (SELU/ELU/RELU):  96.1% |  91.4% |  86.7%, loss (SELU/ELU/RELU): 0.17 | 0.21 | 0.41\n",
      "Global Step:   5900, accuracy (SELU/ELU/RELU):  94.5% |  92.2% |  89.1%, loss (SELU/ELU/RELU): 0.15 | 0.26 | 0.42\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.44% | 75.27% | 73.97%\n",
      "Saved checkpoint.\n",
      "Global Step:   5910, accuracy (SELU/ELU/RELU):  96.9% |  93.0% |  90.6%, loss (SELU/ELU/RELU): 0.15 | 0.19 | 0.30\n",
      "Global Step:   5920, accuracy (SELU/ELU/RELU):  96.1% |  96.1% |  94.5%, loss (SELU/ELU/RELU): 0.14 | 0.16 | 0.25\n",
      "Global Step:   5930, accuracy (SELU/ELU/RELU):  92.2% |  93.0% |  84.4%, loss (SELU/ELU/RELU): 0.28 | 0.25 | 0.45\n",
      "Global Step:   5940, accuracy (SELU/ELU/RELU):  94.5% |  93.0% |  84.4%, loss (SELU/ELU/RELU): 0.17 | 0.24 | 0.45\n",
      "Global Step:   5950, accuracy (SELU/ELU/RELU):  96.9% |  94.5% |  89.1%, loss (SELU/ELU/RELU): 0.14 | 0.20 | 0.31\n",
      "Global Step:   5960, accuracy (SELU/ELU/RELU):  96.1% |  92.2% |  81.2%, loss (SELU/ELU/RELU): 0.18 | 0.26 | 0.49\n",
      "Global Step:   5970, accuracy (SELU/ELU/RELU):  94.5% |  92.2% |  80.5%, loss (SELU/ELU/RELU): 0.17 | 0.22 | 0.43\n",
      "Global Step:   5980, accuracy (SELU/ELU/RELU):  95.3% |  94.5% |  86.7%, loss (SELU/ELU/RELU): 0.21 | 0.21 | 0.38\n",
      "Global Step:   5990, accuracy (SELU/ELU/RELU):  94.5% |  93.8% |  88.3%, loss (SELU/ELU/RELU): 0.19 | 0.22 | 0.33\n",
      "Global Step:   6000, accuracy (SELU/ELU/RELU):  95.3% |  93.0% |  86.7%, loss (SELU/ELU/RELU): 0.18 | 0.23 | 0.38\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.29% | 75.63% | 73.32%\n",
      "Saved checkpoint.\n",
      "Global Step:   6010, accuracy (SELU/ELU/RELU):  95.3% |  93.8% |  83.6%, loss (SELU/ELU/RELU): 0.15 | 0.20 | 0.49\n",
      "Global Step:   6020, accuracy (SELU/ELU/RELU):  96.1% |  93.8% |  88.3%, loss (SELU/ELU/RELU): 0.15 | 0.23 | 0.35\n",
      "Global Step:   6030, accuracy (SELU/ELU/RELU):  94.5% |  91.4% |  79.7%, loss (SELU/ELU/RELU): 0.21 | 0.26 | 0.51\n",
      "Global Step:   6040, accuracy (SELU/ELU/RELU):  98.4% |  95.3% |  87.5%, loss (SELU/ELU/RELU): 0.12 | 0.17 | 0.34\n",
      "Global Step:   6050, accuracy (SELU/ELU/RELU):  95.3% |  93.0% |  83.6%, loss (SELU/ELU/RELU): 0.18 | 0.16 | 0.44\n",
      "Global Step:   6060, accuracy (SELU/ELU/RELU):  96.1% |  93.8% |  93.0%, loss (SELU/ELU/RELU): 0.15 | 0.19 | 0.26\n",
      "Global Step:   6070, accuracy (SELU/ELU/RELU):  92.2% |  93.8% |  90.6%, loss (SELU/ELU/RELU): 0.18 | 0.17 | 0.25\n",
      "Global Step:   6080, accuracy (SELU/ELU/RELU):  93.0% |  90.6% |  87.5%, loss (SELU/ELU/RELU): 0.22 | 0.34 | 0.45\n",
      "Global Step:   6090, accuracy (SELU/ELU/RELU):  94.5% |  91.4% |  87.5%, loss (SELU/ELU/RELU): 0.17 | 0.22 | 0.41\n",
      "Global Step:   6100, accuracy (SELU/ELU/RELU):  96.1% |  93.0% |  89.8%, loss (SELU/ELU/RELU): 0.16 | 0.21 | 0.32\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.29% | 75.68% | 72.64%\n",
      "Saved checkpoint.\n",
      "Global Step:   6110, accuracy (SELU/ELU/RELU):  96.1% |  94.5% |  93.0%, loss (SELU/ELU/RELU): 0.15 | 0.22 | 0.29\n",
      "Global Step:   6120, accuracy (SELU/ELU/RELU):  96.9% |  93.0% |  84.4%, loss (SELU/ELU/RELU): 0.13 | 0.19 | 0.40\n",
      "Global Step:   6130, accuracy (SELU/ELU/RELU):  91.4% |  89.8% |  83.6%, loss (SELU/ELU/RELU): 0.22 | 0.31 | 0.45\n",
      "Global Step:   6140, accuracy (SELU/ELU/RELU):  93.8% |  96.1% |  85.9%, loss (SELU/ELU/RELU): 0.20 | 0.20 | 0.37\n",
      "Global Step:   6150, accuracy (SELU/ELU/RELU):  95.3% |  91.4% |  85.9%, loss (SELU/ELU/RELU): 0.18 | 0.23 | 0.44\n",
      "Global Step:   6160, accuracy (SELU/ELU/RELU):  96.1% |  94.5% |  89.8%, loss (SELU/ELU/RELU): 0.13 | 0.21 | 0.35\n",
      "Global Step:   6170, accuracy (SELU/ELU/RELU):  98.4% |  93.8% |  91.4%, loss (SELU/ELU/RELU): 0.12 | 0.21 | 0.30\n",
      "Global Step:   6180, accuracy (SELU/ELU/RELU):  94.5% |  88.3% |  87.5%, loss (SELU/ELU/RELU): 0.20 | 0.29 | 0.46\n",
      "Global Step:   6190, accuracy (SELU/ELU/RELU):  96.9% |  95.3% |  89.1%, loss (SELU/ELU/RELU): 0.11 | 0.18 | 0.35\n",
      "Global Step:   6200, accuracy (SELU/ELU/RELU):  96.9% |  93.8% |  88.3%, loss (SELU/ELU/RELU): 0.23 | 0.23 | 0.45\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.49% | 76.30% | 73.80%\n",
      "Saved checkpoint.\n",
      "Global Step:   6210, accuracy (SELU/ELU/RELU):  96.1% |  93.0% |  89.8%, loss (SELU/ELU/RELU): 0.13 | 0.22 | 0.34\n",
      "Global Step:   6220, accuracy (SELU/ELU/RELU):  96.9% |  95.3% |  85.9%, loss (SELU/ELU/RELU): 0.16 | 0.20 | 0.41\n",
      "Global Step:   6230, accuracy (SELU/ELU/RELU):  96.1% |  92.2% |  83.6%, loss (SELU/ELU/RELU): 0.16 | 0.23 | 0.48\n",
      "Global Step:   6240, accuracy (SELU/ELU/RELU):  96.9% |  95.3% |  88.3%, loss (SELU/ELU/RELU): 0.14 | 0.17 | 0.32\n",
      "Global Step:   6250, accuracy (SELU/ELU/RELU):  94.5% |  93.0% |  90.6%, loss (SELU/ELU/RELU): 0.19 | 0.18 | 0.36\n",
      "Global Step:   6260, accuracy (SELU/ELU/RELU):  95.3% |  93.0% |  87.5%, loss (SELU/ELU/RELU): 0.22 | 0.24 | 0.44\n",
      "Global Step:   6270, accuracy (SELU/ELU/RELU):  96.1% |  96.1% |  90.6%, loss (SELU/ELU/RELU): 0.12 | 0.15 | 0.31\n",
      "Global Step:   6280, accuracy (SELU/ELU/RELU):  92.2% |  92.2% |  85.2%, loss (SELU/ELU/RELU): 0.21 | 0.20 | 0.37\n",
      "Global Step:   6290, accuracy (SELU/ELU/RELU):  96.1% |  96.1% |  90.6%, loss (SELU/ELU/RELU): 0.13 | 0.15 | 0.27\n",
      "Global Step:   6300, accuracy (SELU/ELU/RELU):  95.3% |  95.3% |  87.5%, loss (SELU/ELU/RELU): 0.15 | 0.18 | 0.37\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.08% | 74.97% | 73.76%\n",
      "Saved checkpoint.\n",
      "Global Step:   6310, accuracy (SELU/ELU/RELU):  96.1% |  96.1% |  92.2%, loss (SELU/ELU/RELU): 0.12 | 0.12 | 0.26\n",
      "Global Step:   6320, accuracy (SELU/ELU/RELU):  95.3% |  93.0% |  86.7%, loss (SELU/ELU/RELU): 0.21 | 0.23 | 0.43\n",
      "Global Step:   6330, accuracy (SELU/ELU/RELU):  93.8% |  93.8% |  89.8%, loss (SELU/ELU/RELU): 0.14 | 0.21 | 0.32\n",
      "Global Step:   6340, accuracy (SELU/ELU/RELU):  96.1% |  94.5% |  82.8%, loss (SELU/ELU/RELU): 0.15 | 0.25 | 0.45\n",
      "Global Step:   6350, accuracy (SELU/ELU/RELU):  94.5% |  94.5% |  89.1%, loss (SELU/ELU/RELU): 0.18 | 0.21 | 0.39\n",
      "Global Step:   6360, accuracy (SELU/ELU/RELU):  96.1% |  94.5% |  93.8%, loss (SELU/ELU/RELU): 0.13 | 0.17 | 0.25\n",
      "Global Step:   6370, accuracy (SELU/ELU/RELU):  96.9% |  93.0% |  90.6%, loss (SELU/ELU/RELU): 0.12 | 0.19 | 0.37\n",
      "Global Step:   6380, accuracy (SELU/ELU/RELU):  95.3% |  93.0% |  85.9%, loss (SELU/ELU/RELU): 0.18 | 0.24 | 0.45\n",
      "Global Step:   6390, accuracy (SELU/ELU/RELU):  96.1% |  97.7% |  89.1%, loss (SELU/ELU/RELU): 0.11 | 0.10 | 0.27\n",
      "Global Step:   6400, accuracy (SELU/ELU/RELU):  94.5% |  91.4% |  88.3%, loss (SELU/ELU/RELU): 0.16 | 0.25 | 0.36\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.51% | 76.31% | 73.55%\n",
      "Saved checkpoint.\n",
      "Global Step:   6410, accuracy (SELU/ELU/RELU):  96.1% |  96.9% |  84.4%, loss (SELU/ELU/RELU): 0.14 | 0.18 | 0.36\n",
      "Global Step:   6420, accuracy (SELU/ELU/RELU):  96.9% |  92.2% |  85.9%, loss (SELU/ELU/RELU): 0.17 | 0.21 | 0.43\n",
      "Global Step:   6430, accuracy (SELU/ELU/RELU):  95.3% |  95.3% |  86.7%, loss (SELU/ELU/RELU): 0.15 | 0.17 | 0.40\n",
      "Global Step:   6440, accuracy (SELU/ELU/RELU):  95.3% |  92.2% |  89.8%, loss (SELU/ELU/RELU): 0.19 | 0.20 | 0.33\n",
      "Global Step:   6450, accuracy (SELU/ELU/RELU):  96.9% |  92.2% |  89.8%, loss (SELU/ELU/RELU): 0.14 | 0.20 | 0.37\n",
      "Global Step:   6460, accuracy (SELU/ELU/RELU):  93.8% |  91.4% |  84.4%, loss (SELU/ELU/RELU): 0.20 | 0.25 | 0.40\n",
      "Global Step:   6470, accuracy (SELU/ELU/RELU):  93.0% |  93.8% |  86.7%, loss (SELU/ELU/RELU): 0.17 | 0.20 | 0.41\n",
      "Global Step:   6480, accuracy (SELU/ELU/RELU):  96.9% |  95.3% |  89.1%, loss (SELU/ELU/RELU): 0.14 | 0.13 | 0.37\n",
      "Global Step:   6490, accuracy (SELU/ELU/RELU):  98.4% |  92.2% |  88.3%, loss (SELU/ELU/RELU): 0.10 | 0.19 | 0.30\n",
      "Global Step:   6500, accuracy (SELU/ELU/RELU):  94.5% |  96.1% |  90.6%, loss (SELU/ELU/RELU): 0.14 | 0.15 | 0.32\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.34% | 76.33% | 74.85%\n",
      "Saved checkpoint.\n",
      "Global Step:   6510, accuracy (SELU/ELU/RELU):  95.3% |  97.7% |  91.4%, loss (SELU/ELU/RELU): 0.13 | 0.11 | 0.24\n",
      "Global Step:   6520, accuracy (SELU/ELU/RELU):  96.1% |  96.1% |  83.6%, loss (SELU/ELU/RELU): 0.15 | 0.20 | 0.41\n",
      "Global Step:   6530, accuracy (SELU/ELU/RELU):  97.7% |  97.7% |  91.4%, loss (SELU/ELU/RELU): 0.09 | 0.14 | 0.26\n",
      "Global Step:   6540, accuracy (SELU/ELU/RELU):  93.8% |  91.4% |  85.9%, loss (SELU/ELU/RELU): 0.23 | 0.22 | 0.41\n",
      "Global Step:   6550, accuracy (SELU/ELU/RELU):  95.3% |  96.9% |  94.5%, loss (SELU/ELU/RELU): 0.11 | 0.11 | 0.23\n",
      "Global Step:   6560, accuracy (SELU/ELU/RELU):  97.7% |  94.5% |  89.1%, loss (SELU/ELU/RELU): 0.11 | 0.14 | 0.25\n",
      "Global Step:   6570, accuracy (SELU/ELU/RELU):  96.1% |  94.5% |  89.8%, loss (SELU/ELU/RELU): 0.12 | 0.19 | 0.33\n",
      "Global Step:   6580, accuracy (SELU/ELU/RELU):  96.1% |  95.3% |  92.2%, loss (SELU/ELU/RELU): 0.10 | 0.14 | 0.28\n",
      "Global Step:   6590, accuracy (SELU/ELU/RELU):  96.1% |  96.9% |  85.2%, loss (SELU/ELU/RELU): 0.10 | 0.15 | 0.32\n",
      "Global Step:   6600, accuracy (SELU/ELU/RELU):  96.1% |  96.1% |  88.3%, loss (SELU/ELU/RELU): 0.15 | 0.18 | 0.31\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.81% | 75.15% | 74.39%\n",
      "Saved checkpoint.\n",
      "Global Step:   6610, accuracy (SELU/ELU/RELU):  95.3% |  96.1% |  93.8%, loss (SELU/ELU/RELU): 0.12 | 0.13 | 0.21\n",
      "Global Step:   6620, accuracy (SELU/ELU/RELU):  94.5% |  93.0% |  87.5%, loss (SELU/ELU/RELU): 0.17 | 0.20 | 0.37\n",
      "Global Step:   6630, accuracy (SELU/ELU/RELU):  96.1% |  95.3% |  90.6%, loss (SELU/ELU/RELU): 0.16 | 0.14 | 0.38\n",
      "Global Step:   6640, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  94.5%, loss (SELU/ELU/RELU): 0.08 | 0.13 | 0.23\n",
      "Global Step:   6650, accuracy (SELU/ELU/RELU):  93.8% |  93.0% |  87.5%, loss (SELU/ELU/RELU): 0.17 | 0.22 | 0.36\n",
      "Global Step:   6660, accuracy (SELU/ELU/RELU):  95.3% |  96.9% |  96.1%, loss (SELU/ELU/RELU): 0.13 | 0.14 | 0.19\n",
      "Global Step:   6670, accuracy (SELU/ELU/RELU):  97.7% |  96.9% |  92.2%, loss (SELU/ELU/RELU): 0.14 | 0.19 | 0.29\n",
      "Global Step:   6680, accuracy (SELU/ELU/RELU):  93.8% |  93.0% |  92.2%, loss (SELU/ELU/RELU): 0.15 | 0.19 | 0.26\n",
      "Global Step:   6690, accuracy (SELU/ELU/RELU):  99.2% |  96.1% |  89.8%, loss (SELU/ELU/RELU): 0.09 | 0.13 | 0.31\n",
      "Global Step:   6700, accuracy (SELU/ELU/RELU):  95.3% |  96.1% |  89.8%, loss (SELU/ELU/RELU): 0.13 | 0.14 | 0.36\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.40% | 75.12% | 73.55%\n",
      "Saved checkpoint.\n",
      "Global Step:   6710, accuracy (SELU/ELU/RELU):  96.1% |  96.9% |  87.5%, loss (SELU/ELU/RELU): 0.11 | 0.14 | 0.31\n",
      "Global Step:   6720, accuracy (SELU/ELU/RELU):  97.7% |  98.4% |  90.6%, loss (SELU/ELU/RELU): 0.09 | 0.12 | 0.29\n",
      "Global Step:   6730, accuracy (SELU/ELU/RELU):  93.0% |  95.3% |  89.1%, loss (SELU/ELU/RELU): 0.16 | 0.16 | 0.42\n",
      "Global Step:   6740, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  90.6%, loss (SELU/ELU/RELU): 0.08 | 0.14 | 0.31\n",
      "Global Step:   6750, accuracy (SELU/ELU/RELU):  99.2% |  94.5% |  93.0%, loss (SELU/ELU/RELU): 0.08 | 0.14 | 0.24\n",
      "Global Step:   6760, accuracy (SELU/ELU/RELU):  96.9% |  97.7% |  95.3%, loss (SELU/ELU/RELU): 0.09 | 0.09 | 0.21\n",
      "Global Step:   6770, accuracy (SELU/ELU/RELU):  97.7% |  95.3% |  94.5%, loss (SELU/ELU/RELU): 0.11 | 0.16 | 0.26\n",
      "Global Step:   6780, accuracy (SELU/ELU/RELU):  96.9% |  97.7% |  93.0%, loss (SELU/ELU/RELU): 0.08 | 0.11 | 0.22\n",
      "Global Step:   6790, accuracy (SELU/ELU/RELU):  96.1% |  95.3% |  92.2%, loss (SELU/ELU/RELU): 0.12 | 0.15 | 0.27\n",
      "Global Step:   6800, accuracy (SELU/ELU/RELU):  96.1% |  95.3% |  86.7%, loss (SELU/ELU/RELU): 0.22 | 0.18 | 0.45\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.41% | 75.66% | 74.47%\n",
      "Saved checkpoint.\n",
      "Global Step:   6810, accuracy (SELU/ELU/RELU):  96.9% |  94.5% |  89.8%, loss (SELU/ELU/RELU): 0.13 | 0.17 | 0.28\n",
      "Global Step:   6820, accuracy (SELU/ELU/RELU):  98.4% |  94.5% |  86.7%, loss (SELU/ELU/RELU): 0.10 | 0.19 | 0.38\n",
      "Global Step:   6830, accuracy (SELU/ELU/RELU):  95.3% |  96.1% |  89.8%, loss (SELU/ELU/RELU): 0.12 | 0.14 | 0.32\n",
      "Global Step:   6840, accuracy (SELU/ELU/RELU):  95.3% |  96.1% |  86.7%, loss (SELU/ELU/RELU): 0.11 | 0.17 | 0.37\n",
      "Global Step:   6850, accuracy (SELU/ELU/RELU):  96.1% |  97.7% |  89.1%, loss (SELU/ELU/RELU): 0.11 | 0.16 | 0.30\n",
      "Global Step:   6860, accuracy (SELU/ELU/RELU):  97.7% |  96.1% |  91.4%, loss (SELU/ELU/RELU): 0.10 | 0.17 | 0.29\n",
      "Global Step:   6870, accuracy (SELU/ELU/RELU):  96.1% |  93.8% |  89.8%, loss (SELU/ELU/RELU): 0.14 | 0.16 | 0.34\n",
      "Global Step:   6880, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  91.4%, loss (SELU/ELU/RELU): 0.05 | 0.07 | 0.23\n",
      "Global Step:   6890, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  93.0%, loss (SELU/ELU/RELU): 0.08 | 0.11 | 0.28\n",
      "Global Step:   6900, accuracy (SELU/ELU/RELU):  96.1% |  94.5% |  88.3%, loss (SELU/ELU/RELU): 0.14 | 0.19 | 0.33\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.85% | 75.21% | 74.28%\n",
      "Saved checkpoint.\n",
      "Global Step:   6910, accuracy (SELU/ELU/RELU):  99.2% |  96.9% |  88.3%, loss (SELU/ELU/RELU): 0.07 | 0.10 | 0.29\n",
      "Global Step:   6920, accuracy (SELU/ELU/RELU):  94.5% |  91.4% |  87.5%, loss (SELU/ELU/RELU): 0.17 | 0.16 | 0.41\n",
      "Global Step:   6930, accuracy (SELU/ELU/RELU):  96.1% |  93.8% |  87.5%, loss (SELU/ELU/RELU): 0.13 | 0.20 | 0.37\n",
      "Global Step:   6940, accuracy (SELU/ELU/RELU):  98.4% |  94.5% |  88.3%, loss (SELU/ELU/RELU): 0.08 | 0.14 | 0.36\n",
      "Global Step:   6950, accuracy (SELU/ELU/RELU):  96.1% |  96.1% |  89.8%, loss (SELU/ELU/RELU): 0.14 | 0.12 | 0.27\n",
      "Global Step:   6960, accuracy (SELU/ELU/RELU):  94.5% |  96.9% |  91.4%, loss (SELU/ELU/RELU): 0.12 | 0.13 | 0.26\n",
      "Global Step:   6970, accuracy (SELU/ELU/RELU):  99.2% |  96.9% |  91.4%, loss (SELU/ELU/RELU): 0.08 | 0.10 | 0.23\n",
      "Global Step:   6980, accuracy (SELU/ELU/RELU):  97.7% |  96.1% |  89.8%, loss (SELU/ELU/RELU): 0.14 | 0.14 | 0.35\n",
      "Global Step:   6990, accuracy (SELU/ELU/RELU):  94.5% |  96.1% |  88.3%, loss (SELU/ELU/RELU): 0.13 | 0.14 | 0.27\n",
      "Global Step:   7000, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  92.2%, loss (SELU/ELU/RELU): 0.08 | 0.06 | 0.22\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.05% | 76.47% | 74.02%\n",
      "Saved checkpoint.\n",
      "Global Step:   7010, accuracy (SELU/ELU/RELU):  98.4% |  98.4% |  91.4%, loss (SELU/ELU/RELU): 0.04 | 0.05 | 0.28\n",
      "Global Step:   7020, accuracy (SELU/ELU/RELU):  95.3% |  96.9% |  93.0%, loss (SELU/ELU/RELU): 0.11 | 0.12 | 0.28\n",
      "Global Step:   7030, accuracy (SELU/ELU/RELU):  98.4% |  95.3% |  95.3%, loss (SELU/ELU/RELU): 0.08 | 0.14 | 0.22\n",
      "Global Step:   7040, accuracy (SELU/ELU/RELU):  97.7% |  95.3% |  93.0%, loss (SELU/ELU/RELU): 0.10 | 0.11 | 0.25\n",
      "Global Step:   7050, accuracy (SELU/ELU/RELU):  97.7% |  93.0% |  92.2%, loss (SELU/ELU/RELU): 0.09 | 0.18 | 0.25\n",
      "Global Step:   7060, accuracy (SELU/ELU/RELU):  99.2% |  96.9% |  89.1%, loss (SELU/ELU/RELU): 0.07 | 0.11 | 0.24\n",
      "Global Step:   7070, accuracy (SELU/ELU/RELU):  96.9% |  94.5% |  90.6%, loss (SELU/ELU/RELU): 0.14 | 0.15 | 0.32\n",
      "Global Step:   7080, accuracy (SELU/ELU/RELU):  99.2% |  95.3% |  85.9%, loss (SELU/ELU/RELU): 0.07 | 0.16 | 0.41\n",
      "Global Step:   7090, accuracy (SELU/ELU/RELU):  99.2% |  96.9% |  84.4%, loss (SELU/ELU/RELU): 0.07 | 0.13 | 0.40\n",
      "Global Step:   7100, accuracy (SELU/ELU/RELU):  96.1% |  97.7% |  95.3%, loss (SELU/ELU/RELU): 0.09 | 0.09 | 0.27\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.20% | 76.00% | 73.55%\n",
      "Saved checkpoint.\n",
      "Global Step:   7110, accuracy (SELU/ELU/RELU):  96.9% |  96.9% |  89.1%, loss (SELU/ELU/RELU): 0.13 | 0.10 | 0.34\n",
      "Global Step:   7120, accuracy (SELU/ELU/RELU):  96.9% |  99.2% |  94.5%, loss (SELU/ELU/RELU): 0.09 | 0.07 | 0.23\n",
      "Global Step:   7130, accuracy (SELU/ELU/RELU):  96.9% |  97.7% |  91.4%, loss (SELU/ELU/RELU): 0.12 | 0.13 | 0.28\n",
      "Global Step:   7140, accuracy (SELU/ELU/RELU):  98.4% |  96.1% |  93.8%, loss (SELU/ELU/RELU): 0.06 | 0.12 | 0.20\n",
      "Global Step:   7150, accuracy (SELU/ELU/RELU):  97.7% |  96.9% |  90.6%, loss (SELU/ELU/RELU): 0.06 | 0.12 | 0.33\n",
      "Global Step:   7160, accuracy (SELU/ELU/RELU):  94.5% |  93.8% |  89.1%, loss (SELU/ELU/RELU): 0.12 | 0.14 | 0.29\n",
      "Global Step:   7170, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  96.1%, loss (SELU/ELU/RELU): 0.05 | 0.11 | 0.21\n",
      "Global Step:   7180, accuracy (SELU/ELU/RELU):  98.4% |  96.1% |  89.8%, loss (SELU/ELU/RELU): 0.09 | 0.10 | 0.25\n",
      "Global Step:   7190, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  93.8%, loss (SELU/ELU/RELU): 0.09 | 0.09 | 0.25\n",
      "Global Step:   7200, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  91.4%, loss (SELU/ELU/RELU): 0.09 | 0.11 | 0.25\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.70% | 75.84% | 74.59%\n",
      "Saved checkpoint.\n",
      "Global Step:   7210, accuracy (SELU/ELU/RELU):  96.9% |  96.9% |  92.2%, loss (SELU/ELU/RELU): 0.11 | 0.09 | 0.24\n",
      "Global Step:   7220, accuracy (SELU/ELU/RELU):  98.4% |  93.8% |  93.0%, loss (SELU/ELU/RELU): 0.08 | 0.20 | 0.25\n",
      "Global Step:   7230, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  90.6%, loss (SELU/ELU/RELU): 0.06 | 0.15 | 0.28\n",
      "Global Step:   7240, accuracy (SELU/ELU/RELU):  97.7% |  97.7% |  89.8%, loss (SELU/ELU/RELU): 0.06 | 0.13 | 0.26\n",
      "Global Step:   7250, accuracy (SELU/ELU/RELU):  93.8% |  96.1% |  88.3%, loss (SELU/ELU/RELU): 0.12 | 0.14 | 0.33\n",
      "Global Step:   7260, accuracy (SELU/ELU/RELU): 100.0% |  96.9% |  94.5%, loss (SELU/ELU/RELU): 0.05 | 0.09 | 0.24\n",
      "Global Step:   7270, accuracy (SELU/ELU/RELU):  97.7% |  98.4% |  90.6%, loss (SELU/ELU/RELU): 0.08 | 0.11 | 0.27\n",
      "Global Step:   7280, accuracy (SELU/ELU/RELU):  98.4% |  96.1% |  91.4%, loss (SELU/ELU/RELU): 0.07 | 0.16 | 0.30\n",
      "Global Step:   7290, accuracy (SELU/ELU/RELU):  99.2% |  96.9% |  92.2%, loss (SELU/ELU/RELU): 0.07 | 0.10 | 0.26\n",
      "Global Step:   7300, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.05 | 0.08 | 0.17\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 77.09% | 75.66% | 73.76%\n",
      "Saved checkpoint.\n",
      "Global Step:   7310, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  88.3%, loss (SELU/ELU/RELU): 0.12 | 0.13 | 0.36\n",
      "Global Step:   7320, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  89.1%, loss (SELU/ELU/RELU): 0.05 | 0.08 | 0.30\n",
      "Global Step:   7330, accuracy (SELU/ELU/RELU):  97.7% |  96.9% |  92.2%, loss (SELU/ELU/RELU): 0.06 | 0.08 | 0.24\n",
      "Global Step:   7340, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  95.3%, loss (SELU/ELU/RELU): 0.08 | 0.08 | 0.16\n",
      "Global Step:   7350, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  88.3%, loss (SELU/ELU/RELU): 0.10 | 0.12 | 0.28\n",
      "Global Step:   7360, accuracy (SELU/ELU/RELU):  97.7% |  96.9% |  90.6%, loss (SELU/ELU/RELU): 0.09 | 0.16 | 0.32\n",
      "Global Step:   7370, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  90.6%, loss (SELU/ELU/RELU): 0.07 | 0.09 | 0.30\n",
      "Global Step:   7380, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  90.6%, loss (SELU/ELU/RELU): 0.06 | 0.15 | 0.30\n",
      "Global Step:   7390, accuracy (SELU/ELU/RELU):  96.9% |  95.3% |  86.7%, loss (SELU/ELU/RELU): 0.11 | 0.14 | 0.41\n",
      "Global Step:   7400, accuracy (SELU/ELU/RELU):  97.7% |  98.4% |  93.8%, loss (SELU/ELU/RELU): 0.09 | 0.10 | 0.26\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.26% | 74.71% | 74.11%\n",
      "Saved checkpoint.\n",
      "Global Step:   7410, accuracy (SELU/ELU/RELU):  96.9% |  95.3% |  90.6%, loss (SELU/ELU/RELU): 0.12 | 0.13 | 0.33\n",
      "Global Step:   7420, accuracy (SELU/ELU/RELU):  99.2% |  95.3% |  89.1%, loss (SELU/ELU/RELU): 0.06 | 0.14 | 0.28\n",
      "Global Step:   7430, accuracy (SELU/ELU/RELU): 100.0% |  96.1% |  92.2%, loss (SELU/ELU/RELU): 0.05 | 0.11 | 0.24\n",
      "Global Step:   7440, accuracy (SELU/ELU/RELU):  99.2% |  96.9% |  89.8%, loss (SELU/ELU/RELU): 0.08 | 0.12 | 0.28\n",
      "Global Step:   7450, accuracy (SELU/ELU/RELU):  96.1% |  93.8% |  95.3%, loss (SELU/ELU/RELU): 0.11 | 0.18 | 0.22\n",
      "Global Step:   7460, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  91.4%, loss (SELU/ELU/RELU): 0.07 | 0.08 | 0.20\n",
      "Global Step:   7470, accuracy (SELU/ELU/RELU):  96.1% |  95.3% |  88.3%, loss (SELU/ELU/RELU): 0.11 | 0.14 | 0.28\n",
      "Global Step:   7480, accuracy (SELU/ELU/RELU):  96.9% |  96.9% |  96.1%, loss (SELU/ELU/RELU): 0.10 | 0.13 | 0.20\n",
      "Global Step:   7490, accuracy (SELU/ELU/RELU):  97.7% |  96.1% |  88.3%, loss (SELU/ELU/RELU): 0.08 | 0.15 | 0.28\n",
      "Global Step:   7500, accuracy (SELU/ELU/RELU):  97.7% | 100.0% |  93.8%, loss (SELU/ELU/RELU): 0.06 | 0.05 | 0.18\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.36% | 75.87% | 73.43%\n",
      "Saved checkpoint.\n",
      "Global Step:   7510, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  90.6%, loss (SELU/ELU/RELU): 0.08 | 0.12 | 0.33\n",
      "Global Step:   7520, accuracy (SELU/ELU/RELU):  99.2% |  95.3% |  87.5%, loss (SELU/ELU/RELU): 0.05 | 0.16 | 0.34\n",
      "Global Step:   7530, accuracy (SELU/ELU/RELU):  96.1% |  99.2% |  92.2%, loss (SELU/ELU/RELU): 0.09 | 0.08 | 0.29\n",
      "Global Step:   7540, accuracy (SELU/ELU/RELU):  99.2% |  97.7% |  94.5%, loss (SELU/ELU/RELU): 0.06 | 0.11 | 0.20\n",
      "Global Step:   7550, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  93.0%, loss (SELU/ELU/RELU): 0.07 | 0.08 | 0.21\n",
      "Global Step:   7560, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  93.0%, loss (SELU/ELU/RELU): 0.07 | 0.10 | 0.23\n",
      "Global Step:   7570, accuracy (SELU/ELU/RELU):  99.2% |  96.9% |  93.8%, loss (SELU/ELU/RELU): 0.06 | 0.09 | 0.26\n",
      "Global Step:   7580, accuracy (SELU/ELU/RELU):  98.4% |  96.1% |  89.8%, loss (SELU/ELU/RELU): 0.08 | 0.11 | 0.24\n",
      "Global Step:   7590, accuracy (SELU/ELU/RELU):  98.4% |  94.5% |  90.6%, loss (SELU/ELU/RELU): 0.07 | 0.14 | 0.30\n",
      "Global Step:   7600, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  93.0%, loss (SELU/ELU/RELU): 0.08 | 0.10 | 0.23\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.65% | 76.41% | 74.91%\n",
      "Saved checkpoint.\n",
      "Global Step:   7610, accuracy (SELU/ELU/RELU):  99.2% |  97.7% |  91.4%, loss (SELU/ELU/RELU): 0.06 | 0.09 | 0.23\n",
      "Global Step:   7620, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  93.0%, loss (SELU/ELU/RELU): 0.04 | 0.09 | 0.25\n",
      "Global Step:   7630, accuracy (SELU/ELU/RELU):  96.9% |  96.1% |  88.3%, loss (SELU/ELU/RELU): 0.10 | 0.09 | 0.27\n",
      "Global Step:   7640, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  88.3%, loss (SELU/ELU/RELU): 0.05 | 0.09 | 0.26\n",
      "Global Step:   7650, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  96.1%, loss (SELU/ELU/RELU): 0.13 | 0.12 | 0.22\n",
      "Global Step:   7660, accuracy (SELU/ELU/RELU):  98.4% |  96.1% |  93.8%, loss (SELU/ELU/RELU): 0.07 | 0.13 | 0.21\n",
      "Global Step:   7670, accuracy (SELU/ELU/RELU):  99.2% |  96.9% |  93.0%, loss (SELU/ELU/RELU): 0.05 | 0.12 | 0.21\n",
      "Global Step:   7680, accuracy (SELU/ELU/RELU):  96.1% |  96.1% |  91.4%, loss (SELU/ELU/RELU): 0.08 | 0.17 | 0.22\n",
      "Global Step:   7690, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  96.1%, loss (SELU/ELU/RELU): 0.07 | 0.09 | 0.14\n",
      "Global Step:   7700, accuracy (SELU/ELU/RELU):  97.7% |  99.2% |  94.5%, loss (SELU/ELU/RELU): 0.06 | 0.06 | 0.19\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.07% | 75.53% | 74.27%\n",
      "Saved checkpoint.\n",
      "Global Step:   7710, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  86.7%, loss (SELU/ELU/RELU): 0.05 | 0.12 | 0.31\n",
      "Global Step:   7720, accuracy (SELU/ELU/RELU):  97.7% |  96.9% |  95.3%, loss (SELU/ELU/RELU): 0.07 | 0.09 | 0.19\n",
      "Global Step:   7730, accuracy (SELU/ELU/RELU):  99.2% |  96.9% |  90.6%, loss (SELU/ELU/RELU): 0.05 | 0.09 | 0.27\n",
      "Global Step:   7740, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  96.1%, loss (SELU/ELU/RELU): 0.06 | 0.09 | 0.18\n",
      "Global Step:   7750, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  93.0%, loss (SELU/ELU/RELU): 0.05 | 0.06 | 0.20\n",
      "Global Step:   7760, accuracy (SELU/ELU/RELU):  98.4% |  98.4% |  92.2%, loss (SELU/ELU/RELU): 0.06 | 0.07 | 0.23\n",
      "Global Step:   7770, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  89.1%, loss (SELU/ELU/RELU): 0.05 | 0.06 | 0.27\n",
      "Global Step:   7780, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  93.8%, loss (SELU/ELU/RELU): 0.05 | 0.06 | 0.20\n",
      "Global Step:   7790, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  93.0%, loss (SELU/ELU/RELU): 0.06 | 0.06 | 0.24\n",
      "Global Step:   7800, accuracy (SELU/ELU/RELU):  97.7% |  96.1% |  90.6%, loss (SELU/ELU/RELU): 0.08 | 0.08 | 0.27\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.55% | 76.00% | 73.58%\n",
      "Saved checkpoint.\n",
      "Global Step:   7810, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  92.2%, loss (SELU/ELU/RELU): 0.05 | 0.06 | 0.24\n",
      "Global Step:   7820, accuracy (SELU/ELU/RELU):  96.9% |  96.9% |  93.8%, loss (SELU/ELU/RELU): 0.10 | 0.11 | 0.20\n",
      "Global Step:   7830, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  94.5%, loss (SELU/ELU/RELU): 0.05 | 0.07 | 0.20\n",
      "Global Step:   7840, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  89.8%, loss (SELU/ELU/RELU): 0.08 | 0.14 | 0.29\n",
      "Global Step:   7850, accuracy (SELU/ELU/RELU):  97.7% |  95.3% |  94.5%, loss (SELU/ELU/RELU): 0.08 | 0.13 | 0.29\n",
      "Global Step:   7860, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  93.8%, loss (SELU/ELU/RELU): 0.03 | 0.06 | 0.20\n",
      "Global Step:   7870, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  92.2%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.25\n",
      "Global Step:   7880, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  94.5%, loss (SELU/ELU/RELU): 0.07 | 0.12 | 0.23\n",
      "Global Step:   7890, accuracy (SELU/ELU/RELU):  96.9% |  97.7% |  92.2%, loss (SELU/ELU/RELU): 0.13 | 0.09 | 0.30\n",
      "Global Step:   7900, accuracy (SELU/ELU/RELU):  98.4% | 100.0% |  95.3%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.19\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.83% | 76.07% | 74.45%\n",
      "Saved checkpoint.\n",
      "Global Step:   7910, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  92.2%, loss (SELU/ELU/RELU): 0.07 | 0.08 | 0.26\n",
      "Global Step:   7920, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  93.0%, loss (SELU/ELU/RELU): 0.07 | 0.09 | 0.20\n",
      "Global Step:   7930, accuracy (SELU/ELU/RELU):  98.4% |  98.4% |  92.2%, loss (SELU/ELU/RELU): 0.06 | 0.06 | 0.23\n",
      "Global Step:   7940, accuracy (SELU/ELU/RELU):  97.7% |  98.4% |  93.8%, loss (SELU/ELU/RELU): 0.06 | 0.07 | 0.18\n",
      "Global Step:   7950, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  93.0%, loss (SELU/ELU/RELU): 0.05 | 0.07 | 0.22\n",
      "Global Step:   7960, accuracy (SELU/ELU/RELU):  98.4% |  98.4% |  91.4%, loss (SELU/ELU/RELU): 0.05 | 0.08 | 0.20\n",
      "Global Step:   7970, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  92.2%, loss (SELU/ELU/RELU): 0.05 | 0.09 | 0.22\n",
      "Global Step:   7980, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  89.8%, loss (SELU/ELU/RELU): 0.03 | 0.08 | 0.24\n",
      "Global Step:   7990, accuracy (SELU/ELU/RELU):  99.2% |  96.1% |  95.3%, loss (SELU/ELU/RELU): 0.05 | 0.13 | 0.20\n",
      "Global Step:   8000, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  93.8%, loss (SELU/ELU/RELU): 0.07 | 0.06 | 0.14\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.91% | 76.00% | 75.41%\n",
      "Saved checkpoint.\n",
      "Global Step:   8010, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  93.0%, loss (SELU/ELU/RELU): 0.05 | 0.05 | 0.18\n",
      "Global Step:   8020, accuracy (SELU/ELU/RELU):  99.2% |  97.7% |  96.1%, loss (SELU/ELU/RELU): 0.05 | 0.07 | 0.13\n",
      "Global Step:   8030, accuracy (SELU/ELU/RELU):  97.7% | 100.0% |  95.3%, loss (SELU/ELU/RELU): 0.05 | 0.04 | 0.18\n",
      "Global Step:   8040, accuracy (SELU/ELU/RELU):  94.5% |  97.7% |  92.2%, loss (SELU/ELU/RELU): 0.18 | 0.18 | 0.38\n",
      "Global Step:   8050, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  98.4%, loss (SELU/ELU/RELU): 0.05 | 0.06 | 0.13\n",
      "Global Step:   8060, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  92.2%, loss (SELU/ELU/RELU): 0.06 | 0.06 | 0.25\n",
      "Global Step:   8070, accuracy (SELU/ELU/RELU): 100.0% |  96.9% |  93.8%, loss (SELU/ELU/RELU): 0.04 | 0.11 | 0.22\n",
      "Global Step:   8080, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  91.4%, loss (SELU/ELU/RELU): 0.03 | 0.06 | 0.22\n",
      "Global Step:   8090, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  90.6%, loss (SELU/ELU/RELU): 0.05 | 0.09 | 0.27\n",
      "Global Step:   8100, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  98.4%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.15\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.98% | 76.28% | 73.93%\n",
      "Saved checkpoint.\n",
      "Global Step:   8110, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  89.1%, loss (SELU/ELU/RELU): 0.05 | 0.06 | 0.27\n",
      "Global Step:   8120, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  93.8%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.19\n",
      "Global Step:   8130, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  91.4%, loss (SELU/ELU/RELU): 0.05 | 0.05 | 0.15\n",
      "Global Step:   8140, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  93.0%, loss (SELU/ELU/RELU): 0.08 | 0.08 | 0.26\n",
      "Global Step:   8150, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  93.0%, loss (SELU/ELU/RELU): 0.04 | 0.08 | 0.21\n",
      "Global Step:   8160, accuracy (SELU/ELU/RELU):  97.7% |  99.2% |  92.2%, loss (SELU/ELU/RELU): 0.05 | 0.07 | 0.19\n",
      "Global Step:   8170, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  92.2%, loss (SELU/ELU/RELU): 0.04 | 0.09 | 0.23\n",
      "Global Step:   8180, accuracy (SELU/ELU/RELU):  97.7% |  97.7% |  93.8%, loss (SELU/ELU/RELU): 0.08 | 0.08 | 0.18\n",
      "Global Step:   8190, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.15\n",
      "Global Step:   8200, accuracy (SELU/ELU/RELU):  99.2% |  97.7% |  95.3%, loss (SELU/ELU/RELU): 0.04 | 0.08 | 0.17\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.87% | 75.22% | 75.08%\n",
      "Saved checkpoint.\n",
      "Global Step:   8210, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.06 | 0.07 | 0.19\n",
      "Global Step:   8220, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  93.0%, loss (SELU/ELU/RELU): 0.06 | 0.08 | 0.20\n",
      "Global Step:   8230, accuracy (SELU/ELU/RELU):  99.2% |  97.7% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.07 | 0.14\n",
      "Global Step:   8240, accuracy (SELU/ELU/RELU):  99.2% |  97.7% |  93.8%, loss (SELU/ELU/RELU): 0.05 | 0.07 | 0.20\n",
      "Global Step:   8250, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  97.7%, loss (SELU/ELU/RELU): 0.05 | 0.04 | 0.14\n",
      "Global Step:   8260, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  93.0%, loss (SELU/ELU/RELU): 0.05 | 0.06 | 0.23\n",
      "Global Step:   8270, accuracy (SELU/ELU/RELU):  98.4% | 100.0% |  92.2%, loss (SELU/ELU/RELU): 0.04 | 0.05 | 0.25\n",
      "Global Step:   8280, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  97.7%, loss (SELU/ELU/RELU): 0.03 | 0.06 | 0.16\n",
      "Global Step:   8290, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  96.9%, loss (SELU/ELU/RELU): 0.05 | 0.07 | 0.11\n",
      "Global Step:   8300, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  93.8%, loss (SELU/ELU/RELU): 0.05 | 0.09 | 0.19\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.43% | 75.54% | 74.48%\n",
      "Saved checkpoint.\n",
      "Global Step:   8310, accuracy (SELU/ELU/RELU):  99.2% |  97.7% |  93.0%, loss (SELU/ELU/RELU): 0.04 | 0.07 | 0.18\n",
      "Global Step:   8320, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  91.4%, loss (SELU/ELU/RELU): 0.03 | 0.07 | 0.22\n",
      "Global Step:   8330, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  94.5%, loss (SELU/ELU/RELU): 0.04 | 0.05 | 0.25\n",
      "Global Step:   8340, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  89.1%, loss (SELU/ELU/RELU): 0.06 | 0.09 | 0.31\n",
      "Global Step:   8350, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  89.1%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.25\n",
      "Global Step:   8360, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  93.8%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.21\n",
      "Global Step:   8370, accuracy (SELU/ELU/RELU):  98.4% |  97.7% | 100.0%, loss (SELU/ELU/RELU): 0.05 | 0.04 | 0.09\n",
      "Global Step:   8380, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.16\n",
      "Global Step:   8390, accuracy (SELU/ELU/RELU): 100.0% |  96.9% |  97.7%, loss (SELU/ELU/RELU): 0.05 | 0.07 | 0.16\n",
      "Global Step:   8400, accuracy (SELU/ELU/RELU):  98.4% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.07 | 0.07 | 0.18\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.92% | 76.66% | 74.42%\n",
      "Saved checkpoint.\n",
      "Global Step:   8410, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  92.2%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.18\n",
      "Global Step:   8420, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  97.7%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.12\n",
      "Global Step:   8430, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  92.2%, loss (SELU/ELU/RELU): 0.06 | 0.06 | 0.19\n",
      "Global Step:   8440, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  93.8%, loss (SELU/ELU/RELU): 0.07 | 0.06 | 0.19\n",
      "Global Step:   8450, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  94.5%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.14\n",
      "Global Step:   8460, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.13\n",
      "Global Step:   8470, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  94.5%, loss (SELU/ELU/RELU): 0.03 | 0.06 | 0.19\n",
      "Global Step:   8480, accuracy (SELU/ELU/RELU):  97.7% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.08 | 0.10 | 0.23\n",
      "Global Step:   8490, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.05 | 0.05 | 0.15\n",
      "Global Step:   8500, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  93.8%, loss (SELU/ELU/RELU): 0.06 | 0.04 | 0.22\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.41% | 76.21% | 75.09%\n",
      "Saved checkpoint.\n",
      "Global Step:   8510, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  96.9%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.17\n",
      "Global Step:   8520, accuracy (SELU/ELU/RELU):  98.4% |  98.4% |  96.9%, loss (SELU/ELU/RELU): 0.05 | 0.06 | 0.16\n",
      "Global Step:   8530, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  96.1%, loss (SELU/ELU/RELU): 0.05 | 0.05 | 0.14\n",
      "Global Step:   8540, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  91.4%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.23\n",
      "Global Step:   8550, accuracy (SELU/ELU/RELU):  98.4% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.14\n",
      "Global Step:   8560, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  93.8%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.20\n",
      "Global Step:   8570, accuracy (SELU/ELU/RELU): 100.0% |  96.9% |  94.5%, loss (SELU/ELU/RELU): 0.03 | 0.07 | 0.14\n",
      "Global Step:   8580, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  92.2%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.23\n",
      "Global Step:   8590, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  94.5%, loss (SELU/ELU/RELU): 0.03 | 0.08 | 0.15\n",
      "Global Step:   8600, accuracy (SELU/ELU/RELU):  97.7% |  97.7% |  96.9%, loss (SELU/ELU/RELU): 0.05 | 0.09 | 0.16\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 77.00% | 75.61% | 73.51%\n",
      "Saved checkpoint.\n",
      "Global Step:   8610, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  94.5%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.14\n",
      "Global Step:   8620, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.07 | 0.19\n",
      "Global Step:   8630, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  90.6%, loss (SELU/ELU/RELU): 0.05 | 0.05 | 0.24\n",
      "Global Step:   8640, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  90.6%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.27\n",
      "Global Step:   8650, accuracy (SELU/ELU/RELU):  99.2% |  97.7% |  95.3%, loss (SELU/ELU/RELU): 0.07 | 0.06 | 0.20\n",
      "Global Step:   8660, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  94.5%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.21\n",
      "Global Step:   8670, accuracy (SELU/ELU/RELU):  97.7% |  96.9% |  92.2%, loss (SELU/ELU/RELU): 0.08 | 0.14 | 0.22\n",
      "Global Step:   8680, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.04 | 0.03 | 0.13\n",
      "Global Step:   8690, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  96.9%, loss (SELU/ELU/RELU): 0.04 | 0.03 | 0.13\n",
      "Global Step:   8700, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.05 | 0.11 | 0.20\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.33% | 75.71% | 74.80%\n",
      "Saved checkpoint.\n",
      "Global Step:   8710, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.13\n",
      "Global Step:   8720, accuracy (SELU/ELU/RELU):  97.7% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.05 | 0.08 | 0.16\n",
      "Global Step:   8730, accuracy (SELU/ELU/RELU):  98.4% |  98.4% |  96.1%, loss (SELU/ELU/RELU): 0.05 | 0.05 | 0.20\n",
      "Global Step:   8740, accuracy (SELU/ELU/RELU):  98.4% |  98.4% |  93.0%, loss (SELU/ELU/RELU): 0.06 | 0.07 | 0.16\n",
      "Global Step:   8750, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  93.0%, loss (SELU/ELU/RELU): 0.02 | 0.05 | 0.18\n",
      "Global Step:   8760, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  94.5%, loss (SELU/ELU/RELU): 0.03 | 0.11 | 0.18\n",
      "Global Step:   8770, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.16\n",
      "Global Step:   8780, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  93.0%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.23\n",
      "Global Step:   8790, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  93.8%, loss (SELU/ELU/RELU): 0.08 | 0.07 | 0.22\n",
      "Global Step:   8800, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  96.9%, loss (SELU/ELU/RELU): 0.04 | 0.03 | 0.13\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.47% | 75.68% | 74.66%\n",
      "Saved checkpoint.\n",
      "Global Step:   8810, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.09\n",
      "Global Step:   8820, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  92.2%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.20\n",
      "Global Step:   8830, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.08 | 0.13\n",
      "Global Step:   8840, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  92.2%, loss (SELU/ELU/RELU): 0.01 | 0.03 | 0.16\n",
      "Global Step:   8850, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  98.4%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.12\n",
      "Global Step:   8860, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.06 | 0.14\n",
      "Global Step:   8870, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.14\n",
      "Global Step:   8880, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.17\n",
      "Global Step:   8890, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.18\n",
      "Global Step:   8900, accuracy (SELU/ELU/RELU):  99.2% |  96.9% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.11 | 0.19\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.92% | 75.72% | 74.87%\n",
      "Saved checkpoint.\n",
      "Global Step:   8910, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  97.7%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.10\n",
      "Global Step:   8920, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  96.9%, loss (SELU/ELU/RELU): 0.02 | 0.07 | 0.10\n",
      "Global Step:   8930, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.10\n",
      "Global Step:   8940, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.06 | 0.18\n",
      "Global Step:   8950, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  94.5%, loss (SELU/ELU/RELU): 0.05 | 0.05 | 0.15\n",
      "Global Step:   8960, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  96.9%, loss (SELU/ELU/RELU): 0.06 | 0.05 | 0.14\n",
      "Global Step:   8970, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  93.8%, loss (SELU/ELU/RELU): 0.02 | 0.07 | 0.19\n",
      "Global Step:   8980, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  96.9%, loss (SELU/ELU/RELU): 0.04 | 0.09 | 0.17\n",
      "Global Step:   8990, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  96.9%, loss (SELU/ELU/RELU): 0.05 | 0.05 | 0.17\n",
      "Global Step:   9000, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.04 | 0.03 | 0.14\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.30% | 75.88% | 75.05%\n",
      "Saved checkpoint.\n",
      "Global Step:   9010, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.08\n",
      "Global Step:   9020, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  93.0%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.17\n",
      "Global Step:   9030, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.11\n",
      "Global Step:   9040, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.06 | 0.13\n",
      "Global Step:   9050, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.11\n",
      "Global Step:   9060, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.20\n",
      "Global Step:   9070, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  94.5%, loss (SELU/ELU/RELU): 0.08 | 0.09 | 0.17\n",
      "Global Step:   9080, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.13\n",
      "Global Step:   9090, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  96.9%, loss (SELU/ELU/RELU): 0.05 | 0.05 | 0.11\n",
      "Global Step:   9100, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  97.7%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.09\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.72% | 75.83% | 74.21%\n",
      "Saved checkpoint.\n",
      "Global Step:   9110, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  94.5%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.20\n",
      "Global Step:   9120, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  94.5%, loss (SELU/ELU/RELU): 0.04 | 0.03 | 0.14\n",
      "Global Step:   9130, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  96.9%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.14\n",
      "Global Step:   9140, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.14\n",
      "Global Step:   9150, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.10\n",
      "Global Step:   9160, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.04 | 0.05 | 0.19\n",
      "Global Step:   9170, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  94.5%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.21\n",
      "Global Step:   9180, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.14\n",
      "Global Step:   9190, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  94.5%, loss (SELU/ELU/RELU): 0.02 | 0.06 | 0.14\n",
      "Global Step:   9200, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.04 | 0.05 | 0.16\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.26% | 75.52% | 74.17%\n",
      "Saved checkpoint.\n",
      "Global Step:   9210, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.03 | 0.02 | 0.07\n",
      "Global Step:   9220, accuracy (SELU/ELU/RELU):  98.4% |  98.4% |  93.0%, loss (SELU/ELU/RELU): 0.06 | 0.04 | 0.18\n",
      "Global Step:   9230, accuracy (SELU/ELU/RELU):  98.4% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.04 | 0.03 | 0.14\n",
      "Global Step:   9240, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  96.9%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.14\n",
      "Global Step:   9250, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  96.9%, loss (SELU/ELU/RELU): 0.04 | 0.05 | 0.12\n",
      "Global Step:   9260, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  94.5%, loss (SELU/ELU/RELU): 0.04 | 0.03 | 0.18\n",
      "Global Step:   9270, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  95.3%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.15\n",
      "Global Step:   9280, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.13\n",
      "Global Step:   9290, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  93.0%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.20\n",
      "Global Step:   9300, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  95.3%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.10\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.59% | 75.74% | 74.19%\n",
      "Saved checkpoint.\n",
      "Global Step:   9310, accuracy (SELU/ELU/RELU):  97.7% |  99.2% |  93.8%, loss (SELU/ELU/RELU): 0.06 | 0.05 | 0.21\n",
      "Global Step:   9320, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.10\n",
      "Global Step:   9330, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.06 | 0.09\n",
      "Global Step:   9340, accuracy (SELU/ELU/RELU):  98.4% |  98.4% |  96.1%, loss (SELU/ELU/RELU): 0.06 | 0.04 | 0.13\n",
      "Global Step:   9350, accuracy (SELU/ELU/RELU):  97.7% |  99.2% |  93.8%, loss (SELU/ELU/RELU): 0.08 | 0.04 | 0.17\n",
      "Global Step:   9360, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  97.7%, loss (SELU/ELU/RELU): 0.03 | 0.02 | 0.11\n",
      "Global Step:   9370, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  96.9%, loss (SELU/ELU/RELU): 0.05 | 0.04 | 0.10\n",
      "Global Step:   9380, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  94.5%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.16\n",
      "Global Step:   9390, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.16\n",
      "Global Step:   9400, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.13\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.74% | 75.54% | 73.55%\n",
      "Saved checkpoint.\n",
      "Global Step:   9410, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.13\n",
      "Global Step:   9420, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.08\n",
      "Global Step:   9430, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.12\n",
      "Global Step:   9440, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  94.5%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.16\n",
      "Global Step:   9450, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  94.5%, loss (SELU/ELU/RELU): 0.02 | 0.05 | 0.15\n",
      "Global Step:   9460, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.01 | 0.02 | 0.14\n",
      "Global Step:   9470, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.12\n",
      "Global Step:   9480, accuracy (SELU/ELU/RELU):  98.4% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.18\n",
      "Global Step:   9490, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.18\n",
      "Global Step:   9500, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.13\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.52% | 75.48% | 73.27%\n",
      "Saved checkpoint.\n",
      "Global Step:   9510, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  99.2%, loss (SELU/ELU/RELU): 0.04 | 0.01 | 0.10\n",
      "Global Step:   9520, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.11\n",
      "Global Step:   9530, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.13\n",
      "Global Step:   9540, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.10\n",
      "Global Step:   9550, accuracy (SELU/ELU/RELU):  98.4% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.04 | 0.03 | 0.11\n",
      "Global Step:   9560, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.14\n",
      "Global Step:   9570, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.12\n",
      "Global Step:   9580, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.05 | 0.02 | 0.10\n",
      "Global Step:   9590, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.01 | 0.03 | 0.11\n",
      "Global Step:   9600, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.13\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.20% | 76.07% | 73.81%\n",
      "Saved checkpoint.\n",
      "Global Step:   9610, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.10\n",
      "Global Step:   9620, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.13\n",
      "Global Step:   9630, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.12\n",
      "Global Step:   9640, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.01 | 0.09\n",
      "Global Step:   9650, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.03 | 0.02 | 0.10\n",
      "Global Step:   9660, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.03 | 0.02 | 0.09\n",
      "Global Step:   9670, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  99.2%, loss (SELU/ELU/RELU): 0.03 | 0.02 | 0.07\n",
      "Global Step:   9680, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.11\n",
      "Global Step:   9690, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.08\n",
      "Global Step:   9700, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.02 | 0.12\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.28% | 75.81% | 74.37%\n",
      "Saved checkpoint.\n",
      "Global Step:   9710, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.11\n",
      "Global Step:   9720, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  99.2%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.08\n",
      "Global Step:   9730, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.12\n",
      "Global Step:   9740, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.10\n",
      "Global Step:   9750, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  93.8%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.14\n",
      "Global Step:   9760, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  93.0%, loss (SELU/ELU/RELU): 0.02 | 0.07 | 0.21\n",
      "Global Step:   9770, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.11\n",
      "Global Step:   9780, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  99.2%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.09\n",
      "Global Step:   9790, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.11\n",
      "Global Step:   9800, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.11\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.74% | 75.78% | 74.55%\n",
      "Saved checkpoint.\n",
      "Global Step:   9810, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.9%, loss (SELU/ELU/RELU): 0.01 | 0.02 | 0.09\n",
      "Global Step:   9820, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  96.9%, loss (SELU/ELU/RELU): 0.02 | 0.06 | 0.14\n",
      "Global Step:   9830, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.06 | 0.13\n",
      "Global Step:   9840, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.01 | 0.02 | 0.07\n",
      "Global Step:   9850, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.12\n",
      "Global Step:   9860, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.14\n",
      "Global Step:   9870, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  94.5%, loss (SELU/ELU/RELU): 0.02 | 0.05 | 0.18\n",
      "Global Step:   9880, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.12\n",
      "Global Step:   9890, accuracy (SELU/ELU/RELU): 100.0% |  98.4% | 100.0%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.07\n",
      "Global Step:   9900, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  99.2%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.08\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.48% | 74.82% | 74.72%\n",
      "Saved checkpoint.\n",
      "Global Step:   9910, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.08 | 0.14\n",
      "Global Step:   9920, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.01 | 0.02 | 0.08\n",
      "Global Step:   9930, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.11\n",
      "Global Step:   9940, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.08\n",
      "Global Step:   9950, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.08\n",
      "Global Step:   9960, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  99.2%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.08\n",
      "Global Step:   9970, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.13\n",
      "Global Step:   9980, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.08\n",
      "Global Step:   9990, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  99.2%, loss (SELU/ELU/RELU): 0.01 | 0.02 | 0.05\n",
      "Global Step:  10000, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.11\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.70% | 75.22% | 73.39%\n",
      "Saved checkpoint.\n",
      "Global Step:  10010, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.08\n",
      "Global Step:  10020, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  98.4%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.06\n",
      "Global Step:  10030, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.08\n",
      "Global Step:  10040, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  98.4%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.10\n",
      "Global Step:  10050, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.10\n",
      "Global Step:  10060, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.11\n",
      "Global Step:  10070, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.10\n",
      "Global Step:  10080, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.01 | 0.03 | 0.09\n",
      "Global Step:  10090, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.11\n",
      "Global Step:  10100, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.04 | 0.02 | 0.09\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.24% | 75.24% | 73.92%\n",
      "Saved checkpoint.\n",
      "Global Step:  10110, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.15\n",
      "Global Step:  10120, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.12\n",
      "Global Step:  10130, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.06\n",
      "Global Step:  10140, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.01 | 0.02 | 0.07\n",
      "Global Step:  10150, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.9%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.08\n",
      "Global Step:  10160, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  99.2%, loss (SELU/ELU/RELU): 0.01 | 0.03 | 0.06\n",
      "Global Step:  10170, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  93.8%, loss (SELU/ELU/RELU): 0.01 | 0.03 | 0.17\n",
      "Global Step:  10180, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.01 | 0.03 | 0.12\n",
      "Global Step:  10190, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.01 | 0.02 | 0.15\n",
      "Global Step:  10200, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.06\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.31% | 76.20% | 74.29%\n",
      "Saved checkpoint.\n",
      "Global Step:  10210, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.10\n",
      "Global Step:  10220, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  96.9%, loss (SELU/ELU/RELU): 0.01 | 0.04 | 0.15\n",
      "Global Step:  10230, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.02 | 0.01 | 0.06\n",
      "Global Step:  10240, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.07\n",
      "Global Step:  10250, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.09\n",
      "Global Step:  10260, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.02 | 0.01 | 0.06\n",
      "Global Step:  10270, accuracy (SELU/ELU/RELU):  98.4% | 100.0% |  99.2%, loss (SELU/ELU/RELU): 0.04 | 0.03 | 0.06\n",
      "Global Step:  10280, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  99.2%, loss (SELU/ELU/RELU): 0.01 | 0.02 | 0.09\n",
      "Global Step:  10290, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  99.2%, loss (SELU/ELU/RELU): 0.01 | 0.01 | 0.05\n",
      "Global Step:  10300, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.12\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.50% | 75.70% | 74.37%\n",
      "Saved checkpoint.\n",
      "Global Step:  10310, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.01 | 0.02 | 0.11\n",
      "Global Step:  10320, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  99.2%, loss (SELU/ELU/RELU): 0.01 | 0.03 | 0.07\n",
      "Global Step:  10330, accuracy (SELU/ELU/RELU): 100.0% | 100.0% | 100.0%, loss (SELU/ELU/RELU): 0.01 | 0.01 | 0.05\n",
      "Global Step:  10340, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.01 | 0.03 | 0.09\n",
      "Global Step:  10350, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  99.2%, loss (SELU/ELU/RELU): 0.01 | 0.01 | 0.06\n",
      "Global Step:  10360, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.01 | 0.01 | 0.07\n",
      "Global Step:  10370, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.11\n",
      "Global Step:  10380, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.08\n",
      "Global Step:  10390, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.01 | 0.01 | 0.07\n",
      "Global Step:  10399, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.01 | 0.02 | 0.11\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.44% | 75.95% | 74.59%\n",
      "Saved checkpoint.\n"
     ]
    }
   ],
   "source": [
    "from time import time\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "# Some Tensorflow configuration\n",
    "tf_config = tf.ConfigProto()\n",
    "tf_config.gpu_options.allow_growth = True\n",
    "\n",
    "# Initialize Dataset\n",
    "train_x, train_y, train_l = get_data_set(\"train\", cifar=10)\n",
    "test_x, test_y, test_l = get_data_set(\"test\", cifar=10)\n",
    "\n",
    "# step counter\n",
    "global_step = tf.Variable(initial_value=0, name='global_step', trainable=False)\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "with tf.Session(config=tf_config) as sess:\n",
    "    try:\n",
    "        print(\"Trying to restore last checkpoint ...\")\n",
    "        last_chk_path = tf.train.latest_checkpoint(checkpoint_dir=_SAVE_PATH)\n",
    "        saver.restore(sess, save_path=last_chk_path)\n",
    "        print(\"Restored checkpoint from:\", last_chk_path)\n",
    "    except:\n",
    "        print(\"Failed to restore checkpoint. Initializing variables instead.\")\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "    if _ITERATION != 0:\n",
    "        train_loss, train_accuracy, test_accuracy = train(\n",
    "            sess, _ITERATION, train_x, train_y, test_x, test_y, \n",
    "            models={\"relu\": relu, \"selu\": selu, \"elu\": elu}, \n",
    "            global_step=global_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IeUL4zE1XFbgiha4K0vNaGwJ7fX4hlxQWcZNfR7u7c/MYWlLK09mRlQL3kCgl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b0a527cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NnwPhcXgY0W0EX6z6giO6HGE5JiGbfiehopvaAYMHD2b69OmsX6+FQc6aNYvG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b047020f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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C8cHKn1C5qTJ9OVSADu/dua2HwKNSDMOA5YV+jxdCjAJOAP+WUibeWkEI8Qrw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\naGJQSinVgiYGpZRSLfx/ayTonSNKxEAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b04655e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Training Loss, Training Accuracy and Test Accuracy for the three activation functions\n",
    "plot(train_loss, train_accuracy, test_accuracy)        "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
